MINLPLib

A Library of Mixed-Integer and Continuous Nonlinear Programming Instances

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This page lists for every application of MINLPLib instances the associated instances.

• demo7

• alkyl
• process

• util

• johnall
• saa_2

• autocorr_bern20-03
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern20-05
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern20-10
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern20-15
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern25-03
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern25-06
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern25-13
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern25-19
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern25-25
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern30-04
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern30-08
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern30-15
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern30-23
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern30-30
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern35-04
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern35-09
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern35-18
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern35-26
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern35-35fix
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions. This instance is the corrected version of autocorr_bern35-35, in which the binary conditions on most variables had been omitted.
• autocorr_bern40-05
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern40-10
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern40-20
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern40-30
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern40-40
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern45-05
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern45-11
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern45-23
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern45-34
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern45-45
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern50-06
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern50-13
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern50-25
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern50-38
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern50-50
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern55-06
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern55-14
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern55-28
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern55-41
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern55-55
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern60-08
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern60-15
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern60-30
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern60-45
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.
• autocorr_bern60-60
  
  degree-four model for low autocorrelated binary sequences This instance arises in theoretical physics. Determining a ground state in the so-called Bernasconi model amounts to minimizing a degree-four energy function over variables taking values in {+1,-1}. Here, the energy function is expressed in 0/1 variables. The model contains symmetries, leading to multiple optimum solutions.

• batch
• batchdes
• contvar
• enpro48pb
  
  enpro48 with added bounds to avoid evaluation errors
• enpro56pb
  
  enpro56 with added bounds to avoid evaluation errors
• ravempb
  
  ravem with added bounds to avoid evaluation errors

• pedigree_ex1058
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_ex485
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_ex485_2
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_sim2000
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_sim400
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_sp_top4_250
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_sp_top4_300
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_sp_top4_350tr
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_sp_top5_200
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint
• pedigree_sp_top5_250
  
  Optimize selection of a fixed-size breeding population under a relatedness constraint

• casctanks

• catmix100
• catmix200
• catmix400
• catmix800

• gasoil100
• gasoil200
• gasoil400
• gasoil50

• forest

• chem
• wallfix
  
  This instance was generated from an fixed version of model wall. Thanks to Johan Löfberg for pointing out this bug.

• spring

• color_lab2_4x0
  
  A generalization of the vertex coloring problem that models the assignment of test variants of a written exam to the desks of a classroom in such a way that desks that are close-by receive different variants.
• color_lab3_3x0
  
  A generalization of the vertex coloring problem that models the assignment of test variants of a written exam to the desks of a classroom in such a way that desks that are close-by receive different variants.
• color_lab3_4x0
  
  A generalization of the vertex coloring problem that models the assignment of test variants of a written exam to the desks of a classroom in such a way that desks that are close-by receive different variants.
• color_lab6b_4x20
  
  A generalization of the vertex coloring problem that models the assignment of test variants of a written exam to the desks of a classroom in such a way that desks that are close-by receive different variants.

• gabriel01
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.
• gabriel02
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.
• gabriel04
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.
• gabriel05
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.
• gabriel06
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.
• gabriel07
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.
• gabriel08
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.
• gabriel09
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.
• gabriel10
  
  The question to answer is to find the minimum value of k such that the k-Gabriel graph of any set of points in the plane is Hamiltonian. A known upper bound on k is 10. Infeasibility of this instance would give some indication for lower bounds on k.

• maxcsp-ehi-85-297-12
  
  Binary Max-CSP (find a complete instantiation of variables which maximizes the number of satisfied constraints) instance with table constraint from CSP 2008 Competition.
• maxcsp-ehi-85-297-36
  
  Binary Max-CSP (find a complete instantiation of variables which maximizes the number of satisfied constraints) instance with table constraint from CSP 2008 Competition.
• maxcsp-ehi-85-297-71
  
  Binary Max-CSP (find a complete instantiation of variables which maximizes the number of satisfied constraints) instance with table constraint from CSP 2008 Competition.
• maxcsp-ehi-90-315-70
  
  Binary Max-CSP (find a complete instantiation of variables which maximizes the number of satisfied constraints) instance with table constraint from CSP 2008 Competition.
• maxcsp-geo50-20-d4-75-36
  
  Binary Max-CSP (find a complete instantiation of variables which maximizes the number of satisfied constraints) instance with table constraint from CSP 2008 Competition.
• maxcsp-langford-3-11
  
  Binary Max-CSP (find a complete instantiation of variables which maximizes the number of satisfied constraints) instance with table constraint from CSP 2008 Competition.

• crossdock_15x7
• crossdock_15x8

• crudeoil_lee1_05
  
  Generated by running Scheduler.gms with LeeCrudeOil1 and for 5 slots
• crudeoil_lee1_06
  
  Generated by running Scheduler.gms with LeeCrudeOil1 and for 6 slots
• crudeoil_lee1_07
  
  Generated by running Scheduler.gms with LeeCrudeOil1 and for 7 slots
• crudeoil_lee1_08
  
  Generated by running Scheduler.gms with LeeCrudeOil1 and for 8 slots
• crudeoil_lee1_09
  
  Generated by running Scheduler.gms with LeeCrudeOil1 and for 9 slots
• crudeoil_lee1_10
  
  Generated by running Scheduler.gms with LeeCrudeOil1 and for 10 slots
• crudeoil_lee2_05
  
  Generated by running Scheduler.gms with LeeCrudeOil2 and for 5 slots
• crudeoil_lee2_06
  
  Generated by running Scheduler.gms with LeeCrudeOil2 and for 6 slots
• crudeoil_lee2_07
  
  Generated by running Scheduler.gms with LeeCrudeOil2 and for 7 slots
• crudeoil_lee2_08
  
  Generated by running Scheduler.gms with LeeCrudeOil2 and for 8 slots
• crudeoil_lee2_09
  
  Generated by running Scheduler.gms with LeeCrudeOil2 and for 9 slots
• crudeoil_lee2_10
  
  Generated by running Scheduler.gms with LeeCrudeOil2 and for 10 slots
• crudeoil_lee3_05
  
  Generated by running Scheduler.gms with LeeCrudeOil3 and for 5 slots
• crudeoil_lee3_06
  
  Generated by running Scheduler.gms with LeeCrudeOil3 and for 6 slots
• crudeoil_lee3_07
  
  Generated by running Scheduler.gms with LeeCrudeOil3 and for 7 slots
• crudeoil_lee3_08
  
  Generated by running Scheduler.gms with LeeCrudeOil3 and for 8 slots
• crudeoil_lee3_09
  
  Generated by running Scheduler.gms with LeeCrudeOil3 and for 9 slots
• crudeoil_lee3_10
  
  Generated by running Scheduler.gms with LeeCrudeOil3 and for 10 slots
• crudeoil_lee4_05
  
  Generated by running Scheduler.gms with LeeCrudeOil4 and for 5 slots
• crudeoil_lee4_06
  
  Generated by running Scheduler.gms with LeeCrudeOil4 and for 6 slots
• crudeoil_lee4_07
  
  Generated by running Scheduler.gms with LeeCrudeOil4 and for 7 slots
• crudeoil_lee4_08
  
  Generated by running Scheduler.gms with LeeCrudeOil4 and for 8 slots
• crudeoil_lee4_09
  
  Generated by running Scheduler.gms with LeeCrudeOil4 and for 9 slots
• crudeoil_lee4_10
  
  Generated by running Scheduler.gms with LeeCrudeOil4 and for 10 slots
• crudeoil_li01
• crudeoil_li02
• crudeoil_li03
• crudeoil_li05
• crudeoil_li06
• crudeoil_li11
• crudeoil_li21

• carton7
• carton9

• parallel

• csched1
• csched1a
  
  Corrected version of csched1. The printed version of the paper had some data inconsistencies. The objective of the models also has been reformulated.
• csched2
• csched2a
  
  Corrected version of csched2. The printed version of the paper had some data inconsistencies. The objective of the models also has been reformulated.

• densitymod

• jit1
  
  This just-in-time flowshop problem involves P products and S stages. Each stage contains identical equipment performing the same type of operation on different products. The objective is to minimize the total equipment related cost.

• unitcommit1
• unitcommit2

• edgecross10-010
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross10-020
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross10-030
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross10-040
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross10-050
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross10-060
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross10-070
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross10-080
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross10-090
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-019
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-039
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-058
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-078
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-098
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-117
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-137
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-156
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross14-176
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross20-040
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross20-080
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross22-048
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross22-096
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross24-057
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.
• edgecross24-115
  
  edge-crossing minimization in bipartite graphs modeled as a quadratic linear ordering problem This instance is for the edge crossing minimization problem in a bipartite graph G. G has to be drawn in the plane so that the nodes of its two shores are placed on two parallel horizontal lines. The task is to minimize the number of edge crossings by permuting the order of nodes on each layer, assuming that all edges are drawn as straight lines. The problem can be modeled as a quadratic objective over linear ordering variables.

• torsion100
• torsion25
• torsion50
• torsion75

• deb10
• deb6
• deb7
• deb8
• deb9
• var_con10
• var_con5

• acopf_case1354pegase_qcqp
  
  AC Optimal Power Flow: Given an undirected network, where at every node electric power is generated or demanded and edges model transmission lines, the goal is to minimize the cost of power generation subject to network demand, the nonlinear physics of power flows, and operational constraints. European system
• acopf_case13659pegase_qcqp
  
  AC Optimal Power Flow: Given an undirected network, where at every node electric power is generated or demanded and edges model transmission lines, the goal is to minimize the cost of power generation subject to network demand, the nonlinear physics of power flows, and operational constraints. European system
• acopf_case6468rte_qcqp
  
  AC Optimal Power Flow: Given an undirected network, where at every node electric power is generated or demanded and edges model transmission lines, the goal is to minimize the cost of power generation subject to network demand, the nonlinear physics of power flows, and operational constraints. French system
• acopf_case6515rte_qcqp
  
  AC Optimal Power Flow: Given an undirected network, where at every node electric power is generated or demanded and edges model transmission lines, the goal is to minimize the cost of power generation subject to network demand, the nonlinear physics of power flows, and operational constraints. French system
• acopf_case9241pegase_qcqp
  
  AC Optimal Power Flow: Given an undirected network, where at every node electric power is generated or demanded and edges model transmission lines, the goal is to minimize the cost of power generation subject to network demand, the nonlinear physics of power flows, and operational constraints. European system
• acopf_caseactivsg25k_qcqp
  
  AC Optimal Power Flow: Given an undirected network, where at every node electric power is generated or demanded and edges model transmission lines, the goal is to minimize the cost of power generation subject to network demand, the nonlinear physics of power flows, and operational constraints. Synthetic US power system model
• acopf_caseactivsg70k_qcqp
  
  AC Optimal Power Flow: Given an undirected network, where at every node electric power is generated or demanded and edges model transmission lines, the goal is to minimize the cost of power generation subject to network demand, the nonlinear physics of power flows, and operational constraints. Synthetic US power system model
• powerflow0009p
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow0009r
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• powerflow0014p
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow0014r
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• powerflow0030p
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow0030r
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• powerflow0039p
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow0039r
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• powerflow0057p
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow0057r
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• powerflow0118p
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow0118r
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• powerflow0300p
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow0300r
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• powerflow2383wpp
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow2383wpr
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• powerflow2736spp
  
  Optimal Power Flow problem modeled using trigonometric functions (polar coordinates)
• powerflow2736spr
  
  Optimal Power Flow problem modeled using quadratic functions (rectangular coordinates)
• transswitch0009p
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch0009r
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)
• transswitch0014p
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch0014r
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)
• transswitch0030p
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch0030r
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)
• transswitch0039p
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch0039r
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)
• transswitch0057p
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch0057r
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)
• transswitch0118p
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch0118r
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)
• transswitch0300p
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch0300r
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)
• transswitch2383wpp
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch2383wpr
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)
• transswitch2736spp
  
  Optimal Transmission Switching problem modeled using trigonometric functions (polar coordinates)
• transswitch2736spr
  
  Optimal Transmission Switching problem modeled using quadratic functions (rectangular coordinates)

• gams02
  
  A GAMS client model forwarded by M. Bussieck 12th Feb 2015 and replaced by an updated version on 26th March 2015.

• elec100
• elec200
• elec25
• elec50

• etamac

• emfl050_3_3
  
  Given a set of 50 existing facilities, we compute the coordinates of 9 new facilities on a rectangular grid subject to minimizing the weighted sum of the euclidian distances between facilities.
• emfl050_5_5
  
  Given a set of 50 existing facilities, we compute the coordinates of 25 new facilities on a rectangular grid subject to minimizing the weighted sum of the euclidian distances between facilities.
• emfl100_3_3
  
  Given a set of 100 existing facilities, we compute the coordinates of 9 new facilities on a rectangular grid subject to minimizing the weighted sum of the euclidian distances between facilities.
• emfl100_5_5
  
  Given a set of 100 existing facilities, we compute the coordinates of 25 new facilities on a rectangular grid subject to minimizing the weighted sum of the euclidian distances between facilities.
• sfacloc1_2_80
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 2 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.80.
• sfacloc1_2_90
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 2 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.90.
• sfacloc1_2_95
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 2 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.95.
• sfacloc1_3_80
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 3 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.80.
• sfacloc1_3_90
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 3 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.90.
• sfacloc1_3_95
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 3 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.95.
• sfacloc1_4_80
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 4 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.80.
• sfacloc1_4_90
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 4 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.90.
• sfacloc1_4_95
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 4 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer may be served by more than one facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.95.
• sfacloc2_2_80
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 2 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.80.
• sfacloc2_2_90
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 2 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.90.
• sfacloc2_2_95
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 2 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.95.
• sfacloc2_3_80
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 3 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.80.
• sfacloc2_3_90
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 3 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.90.
• sfacloc2_3_95
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 3 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.95.
• sfacloc2_4_80
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 4 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.80.
• sfacloc2_4_90
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 4 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.90.
• sfacloc2_4_95
  
  Probabilistic Facility Location and Assignment with Random Demand (given by 5000 scenarios), where 4 facilities can be opened anywhere in the Euclidean plane (distances are measured with the Manhattan (or L1) metric), the facilities are capacitated and each customer is served by a single facility. The objective is to minimize an upper-bound on the weighted total-distance (i.e., the sum of the product of the demand of each customer times the distance to the facility serving that customer) such that this bound is satisfied with a reliability level of 0.95.
• squfl010-025
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl010-025persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl010-025.
• squfl010-040
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl010-040persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl010-040.
• squfl010-080
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl010-080persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl010-080.
• squfl015-060
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl015-060persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl015-060.
• squfl015-080
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl015-080persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl015-080.
• squfl020-040
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl020-040persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl020-040.
• squfl020-050
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl020-050persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl020-050.
• squfl020-150
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl020-150persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl020-150.
• squfl025-025
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl025-025persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl025-025.
• squfl025-030
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl025-030persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl025-030.
• squfl025-040
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl025-040persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl025-040.
• squfl030-100
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl030-100persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl030-100.
• squfl030-150
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl030-150persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl030-150.
• squfl040-080
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer.
• squfl040-080persp
  
  Separable quadratic uncapacitated facility location problem. A set of customers, each having unit demand, has to be satisfied by open facilities. The objective is to minimize the sum of the fixed cost for operating facilities and the shipping cost which is proportional to the square of the quantity delivered to each customer. Perspective reformulation of squfl040-080.

• cecil_13
• ortez

• chance

• feedtray2

• feedtray

• chakra
• fin2bb
• hhfair
• meanvarx

• 4stufen
• beuster

• celar6-sub0
  
  A radio-link frequency assignment problem formulated as a cost-function network, aka weighted constraint satisfaction problem.

• gastrans
  
  The problem of distributing gas through a network of pipelines is formulated as a cost minimization subject to nonlinear flow-pressure relations, material balances, and pressure bounds. The Belgian gas network is used as an example.
• gastrans040
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH.
• gastrans135
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 135 nodes, 141 pipes, and 29 compressor stations.
• gastrans582_cold13
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_cold13_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_cold13, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_cold17
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_cold17_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_cold17, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_cool12
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_cool12_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_cool12, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_cool14
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_cool14_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_cool14, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_freezing27
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_freezing27_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_freezing27, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_freezing30
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_freezing30_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_freezing30, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_mild10
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_mild10_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_mild10, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_mild11
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_mild11_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_mild11, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_warm15
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_warm15_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_warm15, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastrans582_warm31
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes".
• gastrans582_warm31_95
  
  Given gas flow and pressure specifications at entries and exits of a realistic gas network, the problem is to find a feasible network flow or to prove that none exists. The data is based on a real-world network from the gas transport company Open Grid Europe GmbH. The network consists of 582 nodes, 278 pipes, 5 compressor stations, 23 control valve stations, 8 resistors, 26 valves, and 269 "short pipes". Compared to instance gastrans582_warm31, in this instance the nominations have been scaled to 95% of the flow amount, i.e., the supplied (discharged) amount of gas at every entry (exit) has been scaled by 0.95.
• gastransnlp
  
  The problem of distributing gas through a network of pipelines is formulated as a cost minimization subject to nonlinear flow-pressure relations, material balances, and pressure bounds. The Belgian gas network is used as an example.

• gasnet

• gear
• gear2
• gear3
• gear4

• camcns
• gancns
• korcns

• ball_mk2_10
  
  A simple MINLP with a feasible set described by a ball. The basic model over which these variations are made is: min sum_i=1^n x_i s.t. sum_i=1^n (x_i - 0.5)^2 <= (n-1)/4 x integer between -1 and 1. Obvisouly, this problem is infeasible and has no solution. It can be shown that any outer-approximation based method will need 2^n linear inequalities to show infeasibility, see reference. In this instance, the ball is slightly moved to contain the point 0.
• ball_mk2_30
  
  A simple MINLP with a feasible set described by a ball. The basic model over which these variations are made is: min sum_i=1^n x_i s.t. sum_i=1^n (x_i - 0.5)^2 <= (n-1)/4 x integer between -1 and 1. Obvisouly, this problem is infeasible and has no solution. It can be shown that any outer-approximation based method will need 2^n linear inequalities to show infeasibility, see reference. In this instance, the ball is slightly moved to contain the point 0.
• ball_mk3_10
  
  A simple MINLP with a feasible set described by a ball. The basic model over which these variations are made is: min sum_i=1^n x_i s.t. sum_i=1^n (x_i - 0.5)^2 <= (n-1)/4 x integer between -1 and 1. Obvisouly, this problem is infeasible and has no solution. It can be shown that any outer-approximation based method will need 2^n linear inequalities to show infeasibility, see reference. In this instance, the ball is an empty ellipse, but the quadratic form is still diagonal.
• ball_mk3_20
  
  A simple MINLP with a feasible set described by a ball. The basic model over which these variations are made is: min sum_i=1^n x_i s.t. sum_i=1^n (x_i - 0.5)^2 <= (n-1)/4 x integer between -1 and 1. Obvisouly, this problem is infeasible and has no solution. It can be shown that any outer-approximation based method will need 2^n linear inequalities to show infeasibility, see reference. In this instance, the ball is an empty ellipse, but the quadratic form is still diagonal.
• ball_mk3_30
  
  A simple MINLP with a feasible set described by a ball. The basic model over which these variations are made is: min sum_i=1^n x_i s.t. sum_i=1^n (x_i - 0.5)^2 <= (n-1)/4 x integer between -1 and 1. Obvisouly, this problem is infeasible and has no solution. It can be shown that any outer-approximation based method will need 2^n linear inequalities to show infeasibility, see reference. In this instance, the ball is an empty ellipse, but the quadratic form is still diagonal.
• ball_mk4_05
  
  A simple MINLP with a feasible set described by a ball. The basic model over which these variations are made is: min sum_i=1^n x_i s.t. sum_i=1^n (x_i - 0.5)^2 <= (n-1)/4 x integer between -1 and 1. Obvisouly, this problem is infeasible and has no solution. It can be shown that any outer-approximation based method will need 2^n linear inequalities to show infeasibility, see reference. In this instance, the ball is an empty ellipse and the quadratic form is not diagonal.
• ball_mk4_10
  
  A simple MINLP with a feasible set described by a ball. The basic model over which these variations are made is: min sum_i=1^n x_i s.t. sum_i=1^n (x_i - 0.5)^2 <= (n-1)/4 x integer between -1 and 1. Obvisouly, this problem is infeasible and has no solution. It can be shown that any outer-approximation based method will need 2^n linear inequalities to show infeasibility, see reference. In this instance, the ball is an empty ellipse and the quadratic form is not diagonal.
• ball_mk4_15
  
  A simple MINLP with a feasible set described by a ball. The basic model over which these variations are made is: min sum_i=1^n x_i s.t. sum_i=1^n (x_i - 0.5)^2 <= (n-1)/4 x integer between -1 and 1. Obvisouly, this problem is infeasible and has no solution. It can be shown that any outer-approximation based method will need 2^n linear inequalities to show infeasibility, see reference. In this instance, the ball is an empty ellipse and the quadratic form is not diagonal.
• himmel16
• house
• inscribedsquare01
  
  The inscribed square problem, also known as the square peg problem or the Toeplitz' conjecture, is an unsolved question in geometry: Does every plane simple closed curve contain all four vertices of some square? This is true if the curve is convex or piecewise smooth and in other special cases. The problem was proposed by Otto Toeplitz in 1911. See also https://en.wikipedia.org/wiki/Inscribed_square_problem This instance computes a maximal inscribing square for the curve (sin(t)*cos(t), sin(t)*t), t in [-pi,pi].
• inscribedsquare02
  
  The inscribed square problem, also known as the square peg problem or the Toeplitz' conjecture, is an unsolved question in geometry: Does every plane simple closed curve contain all four vertices of some square? This is true if the curve is convex or piecewise smooth and in other special cases. The problem was proposed by Otto Toeplitz in 1911. See also https://en.wikipedia.org/wiki/Inscribed_square_problem This instance computes a maximal inscribing square for the curve (sin(t)*cos(t-t*t), t*sin(t)), t in [-pi,pi].
• inscribedsquare03
  
  The inscribed square problem, also known as the square peg problem or the Toeplitz' conjecture, is an unsolved question in geometry: Does every plane simple closed curve contain all four vertices of some square? This is true if the curve is convex or piecewise smooth and in other special cases. The problem was proposed by Otto Toeplitz in 1911. See also https://en.wikipedia.org/wiki/Inscribed_square_problem This instance computes a maximal inscribing square for the curve (cos(t-t*t)*sin(t)-t*t*sin(2*t+3*abs(t)), sin(t)*t+0.5*t*t*cos(t)), t in [-pi,pi].
• kall_circles_c6a
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circles_c6b
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circles_c6c
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circles_c7a
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circles_c8a
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlespolygons_c1p11
  
  A set of circles and convex polygons are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlespolygons_c1p12
  
  A set of circles and convex polygons are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlespolygons_c1p13
  
  A set of circles and convex polygons are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlespolygons_c1p5a
  
  A set of circles and convex polygons are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlespolygons_c1p5b
  
  A set of circles and convex polygons are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlespolygons_c1p6a
  
  A set of circles and convex polygons are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlesrectangles_c1r11
  
  A set of circles and rectangles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlesrectangles_c1r12
  
  A set of circles and rectangles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlesrectangles_c1r13
  
  A set of circles and rectangles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlesrectangles_c6r1
  
  A set of circles and rectangles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlesrectangles_c6r29
  
  A set of circles and rectangles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_circlesrectangles_c6r39
  
  A set of circles and rectangles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c31
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c32
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c41
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c42
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c51
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c52
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c61
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c62
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c63
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c71
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_congruentcircles_c72
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_diffcircles_10
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_diffcircles_5a
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_diffcircles_5b
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_diffcircles_6
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_diffcircles_7
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_diffcircles_8
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_diffcircles_9
  
  A set of circles are to be cut from rectangular design plates to be produced, or from a set of stocked rectangles of known geometric dimensions. The objective is to minimize the area of the design rectangles. The design plates are subject to lower and upper bounds of their widths and lengths. The objects are free of any orientation restrictions.
• kall_ellipsoids_tc02b
  
  A set of tri-axial ellipsoids, with given semi-axes, is to be packed into a rectangular box; its widths, lengths and height are subject to lower and upper bounds. We want to minimize the volume of this box and seek an overlap-free placement of the ellipsoids which can take any orientation.
• kall_ellipsoids_tc03c
  
  A set of tri-axial ellipsoids, with given semi-axes, is to be packed into a rectangular box; its widths, lengths and height are subject to lower and upper bounds. We want to minimize the volume of this box and seek an overlap-free placement of the ellipsoids which can take any orientation.
• kall_ellipsoids_tc05a
  
  A set of tri-axial ellipsoids, with given semi-axes, is to be packed into a rectangular box; its widths, lengths and height are subject to lower and upper bounds. We want to minimize the volume of this box and seek an overlap-free placement of the ellipsoids which can take any orientation.
• maxmin
• ngone
  
  Find a polygon with 100 sides of maximal area, under the constraint that no two of its vertices are further apart than 1.
• orth_d3m6
  
  computation of the minimal orthogonality measure of a 3x6 matrix with orthonormal rows
• orth_d3m6_pl
  
  computation of the minimal orthogonality measure of a 3x6 matrix with orthonormal rows; formulation based on parametrization via Plücker coordinates
• orth_d4m6_pl
  
  computation of the minimal orthogonality measure of a 4x6 matrix with orthonormal rows; formulation based on parametrization via Plücker coordinates
• p_ball_10b_5p_2d_h
  
  Select 5-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 10 balls with radius one. This is a convex-hull formulation.
• p_ball_10b_5p_2d_m
  
  Select 5-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 10 balls with radius one. This is a big-M formulation.
• p_ball_10b_5p_3d_h
  
  Select 5-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 10 balls with radius one. This is a convex-hull formulation.
• p_ball_10b_5p_3d_m
  
  Select 5-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 10 balls with radius one. This is a big-M formulation.
• p_ball_10b_5p_4d_h
  
  Select 5-points in 4-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 10 balls with radius one. This is a convex-hull formulation.
• p_ball_10b_5p_4d_m
  
  Select 5-points in 4-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 10 balls with radius one. This is a big-M formulation.
• p_ball_10b_7p_3d_h
  
  Select 7-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 10 balls with radius one. This is a convex-hull formulation.
• p_ball_10b_7p_3d_m
  
  Select 7-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 10 balls with radius one. This is a big-M formulation.
• p_ball_15b_5p_2d_h
  
  Select 5-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 15 balls with radius one. This is a convex-hull formulation.
• p_ball_15b_5p_2d_m
  
  Select 5-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 15 balls with radius one. This is a big-M formulation.
• p_ball_20b_5p_2d_h
  
  Select 5-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 20 balls with radius one. This is a convex-hull formulation.
• p_ball_20b_5p_2d_m
  
  Select 5-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 20 balls with radius one. This is a big-M formulation.
• p_ball_20b_5p_3d_h
  
  Select 5-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 20 balls with radius one. This is a convex-hull formulation.
• p_ball_20b_5p_3d_m
  
  Select 5-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 20 balls with radius one. This is a big-M formulation.
• p_ball_30b_10p_2d_h
  
  Select 10-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 30 balls with radius one. This is a convex-hull formulation.
• p_ball_30b_10p_2d_m
  
  Select 10-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 30 balls with radius one. This is a big-M formulation.
• p_ball_30b_5p_2d_h
  
  Select 5-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 30 balls with radius one. This is a convex-hull formulation.
• p_ball_30b_5p_2d_m
  
  Select 5-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 30 balls with radius one. This is a big-M formulation.
• p_ball_30b_5p_3d_h
  
  Select 5-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 30 balls with radius one. This is a convex-hull formulation.
• p_ball_30b_5p_3d_m
  
  Select 5-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 30 balls with radius one. This is a big-M formulation.
• p_ball_30b_7p_2d_h
  
  Select 7-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 30 balls with radius one. This is a convex-hull formulation.
• p_ball_30b_7p_2d_m
  
  Select 7-points in 2-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 30 balls with radius one. This is a big-M formulation.
• p_ball_40b_5p_3d_h
  
  Select 5-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 40 balls with radius one. This is a convex-hull formulation.
• p_ball_40b_5p_3d_m
  
  Select 5-points in 3-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 40 balls with radius one. This is a big-M formulation.
• p_ball_40b_5p_4d_h
  
  Select 5-points in 4-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 40 balls with radius one. This is a convex-hull formulation.
• p_ball_40b_5p_4d_m
  
  Select 5-points in 4-dimensional balls, such that the l1-distance between all points is minimized. Only one point can be assigned to each ball, and in total there are 40 balls with radius one. This is a big-M formulation.
• pointpack02
  
  Find the maximum radius of 2 non-overlapping circles that all lie in the unix-box.
• pointpack04
  
  Find the maximum radius of 4 non-overlapping circles that all lie in the unix-box.
• pointpack06
  
  Find the maximum radius of 6 non-overlapping circles that all lie in the unix-box.
• pointpack08
  
  Find the maximum radius of 8 non-overlapping circles that all lie in the unix-box.
• pointpack10
  
  Find the maximum radius of 10 non-overlapping circles that all lie in the unix-box.
• pointpack12
  
  Find the maximum radius of 12 non-overlapping circles that all lie in the unix-box.
• pointpack14
  
  Find the maximum radius of 14 non-overlapping circles that all lie in the unix-box.
• polygon100
• polygon25
• polygon50
• polygon75
• ringpack_10_1
  
  This is an application from the tube industry, which is called Recursive Circle Packing Problem. The original task is to minimize the total number of rectangles to pack a given set of rings. Unfortunately, this variant is way too difficult for current global solvers (even for examples with 4-5 rings). An easier variant is to minimize free space in a given set of rectangles. This instance is for the variant with 10 rings and 1 rectangle.
• ringpack_10_2
  
  This is an application from the tube industry, which is called Recursive Circle Packing Problem. The original task is to minimize the total number of rectangles to pack a given set of rings. Unfortunately, this variant is way too difficult for current global solvers (even for examples with 4-5 rings). An easier variant is to minimize free space in a given set of rectangles. This instance is for the variant with 10 rings and 2 rectangles.
• ringpack_20_1
  
  This is an application from the tube industry, which is called Recursive Circle Packing Problem. The original task is to minimize the total number of rectangles to pack a given set of rings. Unfortunately, this variant is way too difficult for current global solvers (even for examples with 4-5 rings). An easier variant is to minimize free space in a given set of rectangles. This instance is for the variant with 20 rings and 1 rectangle.
• ringpack_20_2
  
  This is an application from the tube industry, which is called Recursive Circle Packing Problem. The original task is to minimize the total number of rectangles to pack a given set of rings. Unfortunately, this variant is way too difficult for current global solvers (even for examples with 4-5 rings). An easier variant is to minimize free space in a given set of rectangles. This instance is for the variant with 20 rings and 2 rectangles.
• ringpack_20_3
  
  This is an application from the tube industry, which is called Recursive Circle Packing Problem. The original task is to minimize the total number of rectangles to pack a given set of rings. Unfortunately, this variant is way too difficult for current global solvers (even for examples with 4-5 rings). An easier variant is to minimize free space in a given set of rectangles. This instance is for the variant with 20 rings and 3 rectangles.
• ringpack_30_1
  
  This is an application from the tube industry, which is called Recursive Circle Packing Problem. The original task is to minimize the total number of rectangles to pack a given set of rings. Unfortunately, this variant is way too difficult for current global solvers (even for examples with 4-5 rings). An easier variant is to minimize free space in a given set of rectangles. This instance is for the variant with 30 rings and 1 rectangle.
• ringpack_30_2
  
  This is an application from the tube industry, which is called Recursive Circle Packing Problem. The original task is to minimize the total number of rectangles to pack a given set of rings. Unfortunately, this variant is way too difficult for current global solvers (even for examples with 4-5 rings). An easier variant is to minimize free space in a given set of rectangles. This instance is for the variant with 30 rings and 2 rectangles.
• tricp
  
  Triangular Graph Circle Packing

• graphpart_2g-0044-1601
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2g-0055-0062
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2g-0066-0066
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2g-0077-0077
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2g-0088-0088
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2g-0099-9211
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2g-1010-0824
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2pm-0044-0044
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2pm-0055-0055
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2pm-0066-0066
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2pm-0077-0777
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2pm-0088-0888
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_2pm-0099-0999
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3g-0234-0234
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3g-0244-0244
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3g-0333-0333
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3g-0334-0334
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3g-0344-0344
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3g-0444-0444
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3pm-0234-0234
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3pm-0244-0244
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3pm-0333-0333
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3pm-0334-0334
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3pm-0344-0344
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_3pm-0444-0444
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_clique-20
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_clique-30
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_clique-40
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_clique-50
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_clique-60
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• graphpart_clique-70
  
  This is a quadratic model for the graph partitioning problem. The graphs are taken from the publication of Ghaddar et al. We used 3 parts of the partition to generate the quadratic instances. The model assigns each node to one of the three parts. Hence, the model is symmetric, which should probably be used in a solution algorithm.
• sonet17v4
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet18v6
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet19v5
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet20v6
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet21v6
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet22v4
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet22v5
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet23v4
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet23v6
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet24v2
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet24v5
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet25v5
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonet25v6
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.
• sonetgr17
  
  A variant of the graph partitioning problem where the weight of a cluster in the partition depends on the edges incident to its nodes. This instance has its origin in SONET/SDH optical networks.

• glider100
• glider200
• glider400
• glider50

• chain100
• chain200
• chain400
• chain50

• heatexch_gen1
• heatexch_gen2
• heatexch_gen3
• heatexch_spec1
• heatexch_spec2
• heatexch_spec3
• heatexch_trigen
  
  Sustainable Integration of Trigeneration Systems with Heat Exchanger Networks

• minlphi

• hybriddynamic_fixed
  
  Fixed Finite Elements
• hybriddynamic_fixedcc
  
  Fixed Finite Elements
• hybriddynamic_var
  
  Variable Finite Elements
• hybriddynamic_varcc
  
  Variable Finite Elements

• hydro
• hydroenergy1
• hydroenergy2
• hydroenergy3

• hda

• pinene100
• pinene200
• pinene50

• nemhaus

• kissing2
  
  Determining whether 100 unit spheres can be arranged to be adjacent to a central unit sphere in R^8. This is possible if there is a feasible solution with objective value 0.
• knp3-12
  
  Determining whether 12 3-dimensional spheres of radius 1 can be adjacent to a central sphere of radius 1. This is possible, iff the optimal value of this instance is >= 1.
• knp4-24
  
  Determining whether 24 5-dimensional spheres of radius 1 can be adjacent to a central sphere of radius 1. This is possible, iff the optimal value of this instance is >= 1.
• knp5-40
  
  Determining whether 40 5-dimensional spheres of radius 1 can be adjacent to a central sphere of radius 1. This is possible, iff the optimal value of this instance is >= 1.
• knp5-41
  
  Determining whether 41 5-dimensional spheres of radius 1 can be adjacent to a central sphere of radius 1. This is possible, iff the optimal value of this instance is >= 1.
• knp5-42
  
  Determining whether 42 5-dimensional spheres of radius 1 can be adjacent to a central sphere of radius 1. This is possible, iff the optimal value of this instance is >= 1.
• knp5-43
  
  Determining whether 43 5-dimensional spheres of radius 1 can be adjacent to a central sphere of radius 1. This is possible, iff the optimal value of this instance is >= 1.
• knp5-44
  
  Determining whether 44 5-dimensional spheres of radius 1 can be adjacent to a central sphere of radius 1. This is possible, iff the optimal value of this instance is >= 1.

• kriging_peaks-full010
  
  Gaussian process regression for the peaks functions using 10 datapoints. This is the full-space formulation where intermediate variables are defined by additional constraints.
• kriging_peaks-full020
  
  Gaussian process regression for the peaks functions using 20 datapoints. This is the full-space formulation where intermediate variables are defined by additional constraints.
• kriging_peaks-full030
  
  Gaussian process regression for the peaks functions using 30 datapoints. This is the full-space formulation where intermediate variables are defined by additional constraints.
• kriging_peaks-full050
  
  Gaussian process regression for the peaks functions using 50 datapoints. This is the full-space formulation where intermediate variables are defined by additional constraints.
• kriging_peaks-full100
  
  Gaussian process regression for the peaks functions using 100 datapoints. This is the full-space formulation where intermediate variables are defined by additional constraints.
• kriging_peaks-full200
  
  Gaussian process regression for the peaks functions using 200 datapoints. This is the full-space formulation where intermediate variables are defined by additional constraints.
• kriging_peaks-full500
  
  Gaussian process regression for the peaks functions using 500 datapoints. This is the full-space formulation where intermediate variables are defined by additional constraints.
• kriging_peaks-red010
  
  Gaussian process regression for the peaks functions using 10 datapoints. This is the reduced-space formulation where intermediate variables have been reformulated out.
• kriging_peaks-red020
  
  Gaussian process regression for the peaks functions using 20 datapoints. This is the reduced-space formulation where intermediate variables have been reformulated out.
• kriging_peaks-red030
  
  Gaussian process regression for the peaks functions using 30 datapoints. This is the reduced-space formulation where intermediate variables have been reformulated out.
• kriging_peaks-red050
  
  Gaussian process regression for the peaks functions using 50 datapoints. This is the reduced-space formulation where intermediate variables have been reformulated out.
• kriging_peaks-red100
  
  Gaussian process regression for the peaks functions using 100 datapoints. This is the reduced-space formulation where intermediate variables have been reformulated out.
• kriging_peaks-red200
  
  Gaussian process regression for the peaks functions using 200 datapoints. This is the reduced-space formulation where intermediate variables have been reformulated out.
• kriging_peaks-red500
  
  Gaussian process regression for the peaks functions using 500 datapoints. This is the reduced-space formulation where intermediate variables have been reformulated out.

• launch

• clay0203hfsg
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized. Equivalent perspective reformulation of clay0203.
• clay0203m
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized.
• clay0204hfsg
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized. Equivalent perspective reformulation of clay0204.
• clay0204m
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized.
• clay0205hfsg
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized. Equivalent perspective reformulation of clay0205.
• clay0205m
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized.
• clay0303hfsg
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized. Equivalent perspective reformulation of clay0303.
• clay0303m
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized.
• clay0304hfsg
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized. Equivalent perspective reformulation of clay0304.
• clay0304m
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized.
• clay0305hfsg
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized. Equivalent perspective reformulation of clay0305.
• clay0305m
  
  Non overlapping rectangular units must be placed within the confines of certain designated areas such that the cost of connecting these units is minimized.
• faclay20h
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay25
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay30
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay30h
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay33
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay35
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay60
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay70
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay75
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• faclay80
  
  single-row facility layout problem modeled as a quadratic linear ordering problem An instance of the single-row facility layout problem is formally defined by n one-dimensional facilities with given positive lengths and pairwise non-negative weights. The objective is to arrange the facilities so as to minimize the total weighted sum of the center-to-center distances between all pairs of facilities. This problem can be modeled as a quadratic objective over linear ordering variables.
• flay02h
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay02m
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay03h
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay03m
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay04h
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay04m
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay05h
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay05m
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay06h
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• flay06m
  
  Determine the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.
• fo7
  
  Facility Layout
• fo7_2
  
  Facility Layout
• fo7_ar25_1
  
  Optimization of block layout design problems with unequal areas
• fo7_ar2_1
  
  Optimization of block layout design problems with unequal areas
• fo7_ar3_1
  
  Optimization of block layout design problems with unequal areas
• fo7_ar4_1
  
  Optimization of block layout design problems with unequal areas
• fo7_ar5_1
  
  Optimization of block layout design problems with unequal areas
• fo8
  
  Facility Layout
• fo8_ar25_1
  
  Optimization of block layout design problems with unequal areas
• fo8_ar2_1
  
  Optimization of block layout design problems with unequal areas
• fo8_ar3_1
  
  Optimization of block layout design problems with unequal areas
• fo8_ar4_1
  
  Optimization of block layout design problems with unequal areas
• fo8_ar5_1
  
  Optimization of block layout design problems with unequal areas
• fo9
  
  Facility Layout
• fo9_ar25_1
  
  Optimization of block layout design problems with unequal areas
• fo9_ar2_1
  
  Optimization of block layout design problems with unequal areas
• fo9_ar3_1
  
  Optimization of block layout design problems with unequal areas
• fo9_ar4_1
  
  Optimization of block layout design problems with unequal areas
• fo9_ar5_1
  
  Optimization of block layout design problems with unequal areas
• m3
  
  Facility Layout
• m6
  
  Facility Layout
• m7
  
  Facility Layout
• m7_ar25_1
  
  Optimization of block layout design problems with unequal areas
• m7_ar2_1
  
  Optimization of block layout design problems with unequal areas
• m7_ar3_1
  
  Optimization of block layout design problems with unequal areas
• m7_ar4_1
  
  Optimization of block layout design problems with unequal areas
• m7_ar5_1
  
  Optimization of block layout design problems with unequal areas
• no7_ar25_1
  
  Optimization of block layout design problems with unequal areas
• no7_ar2_1
  
  Optimization of block layout design problems with unequal areas
• no7_ar3_1
  
  Optimization of block layout design problems with unequal areas
• no7_ar4_1
  
  Optimization of block layout design problems with unequal areas
• no7_ar5_1
  
  Optimization of block layout design problems with unequal areas
• o7
  
  Facility Layout
• o7_2
  
  Facility Layout
• o7_ar25_1
  
  Optimization of block layout design problems with unequal areas
• o7_ar2_1
  
  Optimization of block layout design problems with unequal areas
• o7_ar3_1
  
  Optimization of block layout design problems with unequal areas
• o7_ar4_1
  
  Optimization of block layout design problems with unequal areas
• o7_ar5_1
  
  Optimization of block layout design problems with unequal areas
• o8_ar4_1
  
  Optimization of block layout design problems with unequal areas
• o9_ar4_1
  
  Optimization of block layout design problems with unequal areas
• slay04h
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay04m
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay05h
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay05m
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay06h
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay06m
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay07h
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay07m
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay08h
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay08m
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay09h
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay09m
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay10h
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.
• slay10m
  
  Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.

• hadamard_4
  
  Maximize determinant of 4 times 4 binary matrix Let a(n) be the maximal determinant of a 0/1-matrix of size n by n. Hadamard proved that a(n) ≤ 2^(-n) (n+1)^((n+1)/2). A Hadamard matrix attains this bound. The Hadamard conjecture states that this is the case if and only if n+1 is 1 or 2 or a multiple of 4. The values of a(n) for small n are known. See the on-line encyclopedia of integer sequences for more information.
• hadamard_5
  
  Maximize determinant of 5 times 5 binary matrix Let a(n) be the maximal determinant of a 0/1-matrix of size n by n. Hadamard proved that a(n) ≤ 2^(-n) (n+1)^((n+1)/2). A Hadamard matrix attains this bound. The Hadamard conjecture states that this is the case if and only if n+1 is 1 or 2 or a multiple of 4. The values of a(n) for small n are known. See the on-line encyclopedia of integer sequences for more information.
• hadamard_6
  
  Maximize determinant of 6 times 6 binary matrix Let a(n) be the maximal determinant of a 0/1-matrix of size n by n. Hadamard proved that a(n) ≤ 2^(-n) (n+1)^((n+1)/2). A Hadamard matrix attains this bound. The Hadamard conjecture states that this is the case if and only if n+1 is 1 or 2 or a multiple of 4. The values of a(n) for small n are known. See the on-line encyclopedia of integer sequences for more information.
• hadamard_7
  
  Maximize determinant of 7 times 7 binary matrix Let a(n) be the maximal determinant of a 0/1-matrix of size n by n. Hadamard proved that a(n) ≤ 2^(-n) (n+1)^((n+1)/2). A Hadamard matrix attains this bound. The Hadamard conjecture states that this is the case if and only if n+1 is 1 or 2 or a multiple of 4. The values of a(n) for small n are known. See the on-line encyclopedia of integer sequences for more information.
• hadamard_8
  
  Maximize determinant of 8 times 8 binary matrix Let a(n) be the maximal determinant of a 0/1-matrix of size n by n. Hadamard proved that a(n) ≤ 2^(-n) (n+1)^((n+1)/2). A Hadamard matrix attains this bound. The Hadamard conjecture states that this is the case if and only if n+1 is 1 or 2 or a multiple of 4. The values of a(n) for small n are known. See the on-line encyclopedia of integer sequences for more information.
• hadamard_9
  
  Maximize determinant of 9 times 9 binary matrix Let a(n) be the maximal determinant of a 0/1-matrix of size n by n. Hadamard proved that a(n) ≤ 2^(-n) (n+1)^((n+1)/2). A Hadamard matrix attains this bound. The Hadamard conjecture states that this is the case if and only if n+1 is 1 or 2 or a multiple of 4. The values of a(n) for small n are known. See the on-line encyclopedia of integer sequences for more information.

• lip

• popdynm100
• popdynm200
• popdynm25
• popdynm50

• prolog

• pricing050
  
  A firm seeks to determine the price of several new products to enter a competitive market.

• eigena2
  
  Given a symmetric matrix A, find an orthogonal matrix Q and diagonal matrix D such that A Q(T) = Q(T) D.

• chimera_k64ising-01
  
  Maximum Cut on Chimera Graphs
• chimera_k64ising-02
  
  Maximum Cut on Chimera Graphs
• chimera_k64maxcut-01
  
  Maximum Cut on Chimera Graphs
• chimera_k64maxcut-02
  
  Maximum Cut on Chimera Graphs
• chimera_lga-01
  
  Maximum Cut on Chimera Graphs
• chimera_lga-02
  
  Maximum Cut on Chimera Graphs
• chimera_mgw-c16-2031-01
  
  Maximum Cut on Chimera Graphs
• chimera_mgw-c16-2031-02
  
  Maximum Cut on Chimera Graphs
• chimera_mgw-c8-439-onc8-001
  
  Maximum Cut on Chimera Graphs
• chimera_mgw-c8-439-onc8-002
  
  Maximum Cut on Chimera Graphs
• chimera_mgw-c8-507-onc8-01
  
  Maximum Cut on Chimera Graphs
• chimera_mgw-c8-507-onc8-02
  
  Maximum Cut on Chimera Graphs
• chimera_mis-01
  
  Maximum Cut on Chimera Graphs
• chimera_mis-02
  
  Maximum Cut on Chimera Graphs
• chimera_rfr-01
  
  Maximum Cut on Chimera Graphs
• chimera_rfr-02
  
  Maximum Cut on Chimera Graphs
• chimera_selby-c16-01
  
  Maximum Cut on Chimera Graphs
• chimera_selby-c16-02
  
  Maximum Cut on Chimera Graphs
• chimera_selby-c8-onc8-01
  
  Maximum Cut on Chimera Graphs
• chimera_selby-c8-onc8-02
  
  Maximum Cut on Chimera Graphs
• ising2_5-300_5555
  
  A one-dimensional ising chain instance from an application in statistical physics.
• toroidal2g20_5555
  
  A 2-dimensional toroidal grid graph with gaussian distributed weights from an application in statistical physics.
• toroidal3g7_6666
  
  A 3-dimensional toroidal grid graph with gaussian distributed weights from an application in statistical physics.

• methanol100
• methanol200
• methanol400
• methanol50

• stockcycle

• primary

• fac1
• fac2
• fac3

• batch0812
• batch0812_nc
  
  Nonconvex variant of batch0812
• batch_nc
  
  Nonconvex variant of batch
• batchs101006m
  
  Determine volume of equipment, number of units to operate in parallel, and locations of intermediate storage tanks.
• batchs121208m
  
  Determine volume of equipment, number of units to operate in parallel, and locations of intermediate storage tanks.
• batchs151208m
  
  Determine volume of equipment, number of units to operate in parallel, and locations of intermediate storage tanks.
• batchs201210m
  
  Determine volume of equipment, number of units to operate in parallel, and locations of intermediate storage tanks.

• blend029
• blend146
• blend480
• blend531
• blend718
• blend721
• blend852

• case_1scv2

• gasprod_sarawak01
  
  Li et al. reformulated the natural gas production model of Selot et al. from a general MINLP to MIQCQP. The modeling files corresponding to these problems have been scaled in accordance with the design of Li et al. The 3 test cases are effectively the same problem with a different number of uncertain scenarios.
• gasprod_sarawak16
  
  Li et al. reformulated the natural gas production model of Selot et al. from a general MINLP to MIQCQP. The modeling files corresponding to these problems have been scaled in accordance with the design of Li et al. The 3 test cases are effectively the same problem with a different number of uncertain scenarios.
• gasprod_sarawak81
  
  Li et al. reformulated the natural gas production model of Selot et al. from a general MINLP to MIQCQP. The modeling files corresponding to these problems have been scaled in accordance with the design of Li et al. The 3 test cases are effectively the same problem with a different number of uncertain scenarios.

• nd_netgen-2000-2-5-a-a-ns_7
  
  Single-commodity Nonlinear Network Design problem
• nd_netgen-2000-3-4-b-a-ns_7
  
  Single-commodity Nonlinear Network Design problem
• nd_netgen-3000-1-1-b-b-ns_7
  
  Single-commodity Nonlinear Network Design problem
• ndcc12
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs.
• ndcc12persp
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs. Perspective reformulation of ndcc12.
• ndcc13
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs.
• ndcc13persp
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs. Perspective reformulation of ndcc13.
• ndcc14
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs.
• ndcc14persp
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs. Perspective reformulation of ndcc14.
• ndcc15
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs.
• ndcc15persp
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs. Perspective reformulation of ndcc15.
• ndcc16
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs.
• ndcc16persp
  
  Multicommodity network design problem with congestion constraints and fixed costs for opening arcs. Perspective reformulation of ndcc16.
• steenbrf
  
  A totally separable nonconvex multi-commodity network problem
• telecomsp_metro
  
  Telecommunication Network Design with Shared Protection
• telecomsp_njlata
  
  Telecommunication Network Design with Shared Protection
• telecomsp_nor_sun
  
  Telecommunication Network Design with Shared Protection
• telecomsp_pacbell
  
  Telecommunication Network Design with Shared Protection

• ann_compressor_exp
  
  Compressor plant model where compressor powers are learned by embedded artificial neural networks. In this variant of ann_compressor_tanh, the tanh(x) activation function has been replaced by 1-2/(exp(2x)+1) (form 3 in paper).
• ann_compressor_tanh
  
  Compressor plant model where compressor powers are learned by embedded artificial neural networks.
• ann_cumene_exp
  
  Optimization of a cumene chemical process with embedded artificial neural network that model the process. In this variant of ann_cumene_tanh, the tanh(x) activation function has been replaced by 1-2/(exp(2x)+1) (form 3 in paper).
• ann_cumene_tanh
  
  Optimization of a cumene chemical process with embedded artificial neural network that model the process.
• ann_fermentation_exp
  
  Fermentation process of glucose to gluconic acid learned and optimized by an embedded artificial neural network. In this variant of ann_fermentation_tanh, the tanh(x) activation function has been replaced by 1-2/(exp(2x)+1) (form 3 in paper).
• ann_fermentation_tanh
  
  Fermentation process of glucose to gluconic acid learned and optimized by an embedded artificial neural network.
• ann_peaks_exp
  
  Peaks test function learned and optimized by an embedded artificial neural network. In this variant of ann_peaks_tanh, the tanh(x) activation function has been replaced by 1-2/(exp(2x)+1) (form 3 in paper).
• ann_peaks_tanh
  
  Peaks test function learned and optimized by an embedded artificial neural network.

• nuclear104
• nuclear10a
• nuclear10b
• nuclear14
• nuclear14a
• nuclear14b
• nuclear25
• nuclear25a
• nuclear25b
• nuclear49
• nuclear49a
• nuclear49b
• nuclearva
• nuclearvb
• nuclearvc
• nuclearvd
• nuclearve
• nuclearvf

• cont6-qq
  
  A quadratic-quadratic control problem. Example 6 from Maurer and Mittelmann (2001).
• dtoc5
  
  A discrete time optimal control (DTOC) problem with 50000 time periods, 1 control variable, and 1 state variable. Problem 5 in Coleman and Liao (1995)
• junkturn
  
  A spacecraft orientation problem as nonlinear optimal control problem by Junkins and Turner.
• optcdeg2
  
  Determine the applied force that restores a damped spring-mass system to equilibrium as fast as possible. Example 5.11 in Murtagh and Saunders (1982).
• optmass
  
  The problem is that of a particle of unit mass moving on a frictionless plane under the action of a controlling force whose magnitude may not exceed unity. The objective function maximizes the particle's final distance from the origin and minimizes its final speed.
• parabol5_2_2
  
  Parabolic Boundary Control Problem with quadratic boundary control
• parabol5_2_3
  
  Parabolic Boundary Control Problem parabol5_2_1, but with different state constraints
• parabol5_2_4
  
  Parabolic Boundary Control Problem with instationary Burgers equation and active control constraints
• parabol_p
  
  Parabolic Boundary Control Problem parabol5_2_1, but the Hessian of the Lagrangian is not convex on the full space
• trainf
  
  Minimization of the energy spent to move a train from the beginning of a flat track to its end in a given time. The train is slowed down by some drag (assumed to be quadratic in the the velocity). The control variables are the acceleration force and the braking force applied on the train.

• ghg_1veh
• ghg_2veh
• ghg_3veh

• spectra2

• lnts100
• lnts200
• lnts400
• lnts50

• multiplants_mtg1a
• multiplants_mtg1b
• multiplants_mtg1c
• multiplants_mtg2
• multiplants_mtg5
• multiplants_mtg6
• multiplants_stg1
• multiplants_stg1a
• multiplants_stg1b
• multiplants_stg1c
• multiplants_stg5
• multiplants_stg6

• oil
• oil2

• pooling_adhya1pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya1stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya1tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya2pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya2stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya2tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya3pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya3stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya3tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya4pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya4stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_adhya4tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_bental4pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_bental4stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_bental4tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_bental5pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_bental5stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_bental5tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_digabel16
• pooling_digabel18
• pooling_digabel19
• pooling_foulds2pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds2stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds2tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds3pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds3stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds3tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds4pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds4stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds4tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds5pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds5stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_foulds5tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly1pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly1stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly1tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly2pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly2stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly2tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly3pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly3stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_haverly3tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_rt2pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_rt2stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_rt2tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa0pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa0stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa0tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa5pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa5stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa5tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa9pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa9stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppa9tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb0pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb0stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb0tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb2pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb2stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb2tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb5pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb5stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppb5tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc0pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc0stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc0tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc1pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc1stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc1tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc3pq
  
  PQ formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc3stp
  
  STP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.
• pooling_sppc3tp
  
  TP formulation of pooling problem. Explicitly added RLT constraints were removed from the original formulation of Alfaki and Haugland.

• crudeoil_pooling_ct1
  
  Scheduling of the unloading crude of incoming marine vessels to a refinery and transfer of the crudes from storage to charging tanks to the crude oil distillation units. Continuous time formulation. Minimization of total costs.
• crudeoil_pooling_ct2
  
  Scheduling of the unloading crude of incoming marine vessels to a refinery and transfer of the crudes from storage to charging tanks to the crude oil distillation units. Continuous time formulation. Maximization of gross margin.
• crudeoil_pooling_ct3
  
  Scheduling of the unloading crude of incoming marine vessels to a refinery and transfer of the crudes from storage to charging tanks to the crude oil distillation units. Continuous time formulation. Minimization of total costs.
• crudeoil_pooling_ct4
  
  Scheduling of the unloading crude of incoming marine vessels to a refinery and transfer of the crudes from storage to charging tanks to the crude oil distillation units. Continuous time formulation. Maximization of gross margin.
• crudeoil_pooling_dt1
  
  Scheduling of the unloading crude of incoming marine vessels to a refinery and transfer of the crudes from storage to charging tanks to the crude oil distillation units. Discrete time formulation. Minimization of total costs.
• crudeoil_pooling_dt2
  
  Scheduling of the unloading crude of incoming marine vessels to a refinery and transfer of the crudes from storage to charging tanks to the crude oil distillation units. Discrete time formulation. Maximization of gross margin.
• crudeoil_pooling_dt3
  
  Scheduling of the unloading crude of incoming marine vessels to a refinery and transfer of the crudes from storage to charging tanks to the crude oil distillation units. Discrete time formulation. Minimization of total costs.
• crudeoil_pooling_dt4
  
  Scheduling of the unloading crude of incoming marine vessels to a refinery and transfer of the crudes from storage to charging tanks to the crude oil distillation units. Discrete time formulation. Maximization of gross margin.
• genpooling_lee1
• genpooling_lee2
• genpooling_meyer04
• genpooling_meyer10
• genpooling_meyer15
• haverly
• pooling_epa1
• pooling_epa2
• pooling_epa3

• alan
• meanvar-orl400_05_e_7
  
  Mean-Variance problem with minimum buy-in and cardinality constraints
• meanvar-orl400_05_e_8
  
  Mean-Variance problem with minimum buy-in and cardinality constraints
• portfol_buyin
• portfol_card
• portfol_classical050_1
• portfol_classical200_2
• portfol_robust050_34
• portfol_robust100_09
• portfol_robust200_03
• portfol_roundlot
• portfol_shortfall050_68
• portfol_shortfall100_04
• portfol_shortfall200_05
• qp3
• ramsey
• smallinvDAXr1b010-011
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr1b020-022
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr1b050-055
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr1b100-110
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr1b150-165
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr1b200-220
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr2b010-011
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr2b020-022
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr2b050-055
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr2b100-110
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr2b150-165
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr2b200-220
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr3b010-011
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr3b020-022
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr3b050-055
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr3b100-110
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr3b150-165
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr3b200-220
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr4b010-011
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr4b020-022
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr4b050-055
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr4b100-110
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr4b150-165
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr4b200-220
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr5b010-011
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr5b020-022
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr5b050-055
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr5b100-110
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr5b150-165
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• smallinvDAXr5b200-220
  
  Small Investor Mean-Variance Portfolio Optimization This is a natural extension of the standard Markovitz Mean-Variance-Optimization (MVO) model by constraints for small investors. The variables in the standard Markovitz model determine which fraction of the investment is made in the repective assets. When the investment is bounded within a range that is not very much bigger than the prices of the assets, one has to take into account that each asset has a minimum unit of which only integer multiples can be traded, e.g. 1, 0.1, or 0.01. Hence, the optimization problem for the small investor has integer variables in addition to the budget constraints that define lower and upper bounds on the investment.
• worst

• chp_partload
• chp_shorttermplan1a
  
  Short-term planning of combined heat and power (CHP) systems
• chp_shorttermplan1b
  
  Short-term planning of combined heat and power (CHP) systems
• chp_shorttermplan2a
  
  Short-term planning of combined heat and power (CHP) systems
• chp_shorttermplan2b
  
  Short-term planning of combined heat and power (CHP) systems
• chp_shorttermplan2c
  
  Short-term planning of combined heat and power (CHP) systems
• chp_shorttermplan2d
  
  Short-term planning of combined heat and power (CHP) systems
• super3t

• procsel

• procurement1large
  
  Optimal Procurement Contract Selection with Price Optimization under Uncertainty for Process Networks
• procurement1mot
  
  Optimal Procurement Contract Selection with Price Optimization under Uncertainty for Process Networks
• procurement2mot
  
  Optimal Procurement Contract Selection with Price Optimization under Uncertainty for Process Networks Constant-Elasticity Model

• ex3pb
  
  ex3 with added bounds to avoid evaluation errors

• kport20
  
  This problem computes minimal cost solutions satisfying the demand of pre-given product portfolios. It determines the number and size of reactors and gives a schedule of how may batches of each product run on each reactor.
• kport40
  
  This problem computes minimal cost solutions satisfying the demand of pre-given product portfolios. It determines the number and size of reactors and gives a schedule of how may batches of each product run on each reactor.

• ex4
• product
• product2

• chenery
• otpop
• pindyck

• elf

• ex1252
• ex1252a

• quantum
  
  Find energy eigenvalues of the anharmonic oscillator with g=1 in the Gaussian and Post-Gaussian variational methods.

• dosemin2d
• dosemin3d

• lop97ic
• lop97icx
  
  Derived from lop97ic by fixing variables that were zero in the relaxed problem.

• rsyn0805hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0805.
• rsyn0805m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0805m02hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0805m02.
• rsyn0805m02m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0805m03hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0805m03.
• rsyn0805m03m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0805m04hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0805m04.
• rsyn0805m04m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0810hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0810.
• rsyn0810m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0810m02hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0810m02.
• rsyn0810m02m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0810m03hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0810m03.
• rsyn0810m03m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0810m04hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0810m04.
• rsyn0810m04m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0815hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0815.
• rsyn0815m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0815m02hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0815m02.
• rsyn0815m02m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0815m03hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0815m03.
• rsyn0815m03m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0815m04hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0815m04.
• rsyn0815m04m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0820hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0820.
• rsyn0820m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0820m02hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0820m02.
• rsyn0820m02m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0820m03hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0820m03.
• rsyn0820m03m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0820m04hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0820m04.
• rsyn0820m04m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0830hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0830.
• rsyn0830m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0830m02hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0830m02.
• rsyn0830m02m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0830m03hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0830m03.
• rsyn0830m03m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0830m04hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0830m04.
• rsyn0830m04m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0840hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0840.
• rsyn0840m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0840m02hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0840m02.
• rsyn0840m02m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0840m03hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0840m03.
• rsyn0840m03m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.
• rsyn0840m04hfsg
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications. Equivalent perspective reformulation of rsyn0840m04.
• rsyn0840m04m
  
  Redesign of existing plants to increase throughput, reduce energy consumption, improve yields, and reduce waste generation. Given limited capital investments to make process improvements and cost estimations over a given time horizon, the problem is to identify the modifications that yield the highest economic improvement which is defined as the income from product sales minus the cost of raw materials, energy, and process modifications.

• rocket100
• rocket200
• rocket400
• rocket50

• radar-2000-10-a-6_lat_7
  
  1-D Sensor Placement Problem, whereby a set of n sensors have to be optimally placed to cover a given area while minimizing the fixed deployment cost plus an energy cost that is linear in the surface covered (and therefore quadratic in its radius).
• radar-3000-10-a-8_lat_7
  
  1-D Sensor Placement Problem, whereby a set of n sensors have to be optimally placed to cover a given area while minimizing the fixed deployment cost plus an energy cost that is linear in the surface covered (and therefore quadratic in its radius).

• sepasequ_complex
• sepasequ_convent

• sssd08-04
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd08-04persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd08-04.
• sssd12-05
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd12-05persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd12-05.
• sssd15-04
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd15-04persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd15-04.
• sssd15-06
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd15-06persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd15-06.
• sssd15-08
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd15-08persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd15-08.
• sssd16-07
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd16-07persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd16-07.
• sssd18-06
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd18-06persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd18-06.
• sssd18-08
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd18-08persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd18-08.
• sssd20-04
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd20-04persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd20-04.
• sssd20-08
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd20-08persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd20-08.
• sssd22-08
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd22-08persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd22-08.
• sssd25-04
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd25-04persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd25-04.
• sssd25-08
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs.
• sssd25-08persp
  
  Stochastic Service System Design. Servers are modeled as M/M/1 queues, and a set of customers must be assigned to the servers which can be operated at different service levels. The objective is to minimize assignment and operating costs. Perspective reformulation of sssd25-08.

• camshape100
• camshape200
• camshape400
• camshape800

• qspp_0_10_0_1_10_1
  
  Quadratic Shortest Path Problem
• qspp_0_11_0_1_10_1
  
  Quadratic Shortest Path Problem
• qspp_0_12_0_1_10_1
  
  Quadratic Shortest Path Problem
• qspp_0_13_0_1_10_1
  
  Quadratic Shortest Path Problem
• qspp_0_14_0_1_10_1
  
  Quadratic Shortest Path Problem
• qspp_0_15_0_1_10_1
  
  Quadratic Shortest Path Problem

• ex1233
• ex1243
• ex1244
• synheat

• cesam2cent
  
  Illustrates a cross entropy technique for estimating the cells of a consistent SAM assuming that the initial data are inconsistent and measured with error.
• cesam2log
  
  Illustrates a cross entropy technique for estimating the cells of a consistent SAM assuming that the initial data are inconsistent and measured with error. This is a variant of cesam2cent where the centropy() function is written explicitly via basis arithmetic functions (log, ...).

• hvycrash

• sporttournament06
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament08
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament10
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament12
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament14
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament16
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament18
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament20
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament22
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament24
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament26
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament28
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament30
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament32
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament34
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament36
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament38
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament40
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament42
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament44
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament46
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament48
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.
• sporttournament50
  
  This is a quadratic model for the max-cut problem. The instance arises when minimizing so-called breaks in sports tournaments.

• least
• like

• shiporig
  
  This model designs a vertically corrugated transverse bulkhead of an oil tanker. The objective is to design for minimum weight and meet stress, moment of inertia and plate thickness constraints. This version of ship corresponds to the current version of ship from the GAMS model library. It corresponds to the original version from the paper, plus an extra lower bound on variable x10.

• supplychain
• supplychainp1_020306
• supplychainp1_022020
• supplychainp1_030510
• supplychainp1_053050
• supplychainr1_020306
• supplychainr1_022020
• supplychainr1_030510
• supplychainr1_053050

• nous1
• nous2

• syn05hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn05.
• syn05m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn05m02hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn05m02.
• syn05m02m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn05m03hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn05m03.
• syn05m03m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn05m04hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn05m04.
• syn05m04m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn10hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn10.
• syn10m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn10m02hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn10m02.
• syn10m02m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn10m03hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn10m03.
• syn10m03m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn10m04hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn10m04.
• syn10m04m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn15hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn15.
• syn15m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn15m02hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn15m02.
• syn15m02m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn15m03hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn15m03.
• syn15m03m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn15m04hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn15m04.
• syn15m04m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn20hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn20.
• syn20m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn20m02hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn20m02.
• syn20m02m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn20m03hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn20m03.
• syn20m03m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn20m04hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn20m04.
• syn20m04m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn30hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn30.
• syn30m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn30m02hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn30m02.
• syn30m02m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn30m03hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn30m03.
• syn30m03m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn30m04hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn30m04.
• syn30m04m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn40hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn40.
• syn40m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn40m02hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn40m02.
• syn40m02m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn40m03hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn40m03.
• syn40m03m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• syn40m04hfsg
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections. Equivalent perspective reformulation of syn40m04.
• syn40m04m
  
  Selection of optimal configuration and parameters for a processing system selected from a superstructure containing alternative processing units and interconnections.
• synthes1
• synthes2
• synthes3

• space25
• space25a
• space960

• tanksize
  
  We discuss a tank design problem for a multi product plant, in which the optimal cycle time and the optimal campaign size are unknown. A mixed integer nonlinear programming formulation is presented, where non-convexities are due to the tank investment cost, storage cost, campaign setup cost and variable production rates. The objective of the optimization model is to minimize the sum of the production cost per ton per product produced. A continuous-time mathematical programming formulation for the problem is implemented with a fixed number of event points.

• routingdelay_bigm
  
  Delay Constrained Routing Problem
• routingdelay_proj
  
  Delay Constrained Routing Problem

• cvxnonsep_normcon20
  
  convex MINLP test problem with non-separable 2-norm constraint see also problem description (PDF).
• cvxnonsep_normcon20r
  
  separable reformulation of convex MINLP test problem with non-separable 2-norm constraint (cvxnonsep_normcon20) see also problem description (PDF).
• cvxnonsep_normcon30
  
  convex MINLP test problem with non-separable 2-norm constraint see also problem description (PDF).
• cvxnonsep_normcon30r
  
  separable reformulation of convex MINLP test problem with non-separable 2-norm constraint (cvxnonsep_normcon30) see also problem description (PDF).
• cvxnonsep_normcon40
  
  convex MINLP test problem with non-separable 2-norm constraint see also problem description (PDF).
• cvxnonsep_normcon40r
  
  separable reformulation of convex MINLP test problem with non-separable 2-norm constraint (cvxnonsep_normcon40) see also problem description (PDF).
• cvxnonsep_nsig20
  
  convex MINLP test problem with non-separable signomial constraint function see also problem description (PDF).
• cvxnonsep_nsig20r
  
  separable reformulation of convex MINLP test problem with non-separable signomial constraint function (cvxnonsep_nsig20) see also problem description (PDF).
• cvxnonsep_nsig30
  
  convex MINLP test problem with non-separable signomial constraint function see also problem description (PDF).
• cvxnonsep_nsig30r
  
  separable reformulation of convex MINLP test problem with non-separable signomial constraint function (cvxnonsep_nsig30) see also problem description (PDF).
• cvxnonsep_nsig40
  
  convex MINLP test problem with non-separable signomial constraint function see also problem description (PDF).
• cvxnonsep_nsig40r
  
  separable reformulation of convex MINLP test problem with non-separable signomial constraint function (cvxnonsep_nsig40) see also problem description (PDF).
• cvxnonsep_pcon20
  
  convex MINLP test problem with non-separable power constraint see also problem description (PDF).
• cvxnonsep_pcon20r
  
  separable reformulation of convex MINLP test problem with non-separable power constraint (cvxnonsep_pcon20) see also problem description (PDF).
• cvxnonsep_pcon30
  
  convex MINLP test problem with non-separable power constraint see also problem description (PDF).
• cvxnonsep_pcon30r
  
  separable reformulation of convex MINLP test problem with non-separable power constraint (cvxnonsep_pcon30) see also problem description (PDF).
• cvxnonsep_pcon40
  
  convex MINLP test problem with non-separable power constraint see also problem description (PDF).
• cvxnonsep_pcon40r
  
  separable reformulation of convex MINLP test problem with non-separable power constraint (cvxnonsep_pcon40) see also problem description (PDF).
• cvxnonsep_psig20
  
  convex MINLP test problem with non-separable signomial objective function see also problem description (PDF).
• cvxnonsep_psig20r
  
  separable reformulation of convex MINLP test problem with non-separable signomial objective function (cvxnonsep_psig20) see also problem description (PDF).
• cvxnonsep_psig30
  
  convex MINLP test problem with non-separable signomial objective function see also problem description (PDF).
• cvxnonsep_psig30r
  
  separable reformulation of convex MINLP test problem with non-separable signomial objective function (cvxnonsep_psig30) see also problem description (PDF).
• cvxnonsep_psig40
  
  convex MINLP test problem with non-separable signomial objective function see also problem description (PDF). In equation e1, two factors were included for x30 by mistake. As the exponents were randomly generated anyway, adding a "fixed" version of the instance will be omitted.
• cvxnonsep_psig40r
  
  separable reformulation of convex MINLP test problem with non-separable signomial objective function (cvxnonsep_psig40) see also problem description (PDF).
• fct
• himmel11
• hs62
• mathopt1
• mathopt2
• mathopt3
• mathopt4
• mathopt5_1
• mathopt5_2
• mathopt5_3
• mathopt5_4
• mathopt5_5
• mathopt5_6
• mathopt5_7
• mathopt5_8
• mathopt6
  
  The Hundred-dollar, Hundred-digit Challenge Problems as stated by N. Trefethen, Oxford University.
• mhw4d
• rbrock
• trig
• trigx

• topopt-cantilever_60x40_50
  
  Topology Optimization of 2D structures
• topopt-mbb_60x40_50
  
  Topology Optimization of 2D structures
• topopt-zhou-rozvany_75
  
  Topology Optimization of 2D structures

• tspn05
• tspn08
• tspn10
• tspn12
• tspn15

• ex1263
• ex1263a
• ex1264
• ex1264a
• ex1265
• ex1265a
• ex1266
• ex1266a
• tln12
• tln2
• tln4
• tln5
• tln6
• tln7
• tloss
• tls12
• tls2
• tls4
• tls5
• tls6
• tls7
• tltr

• dispatch
• eniplac
• fuel
• unitcommit_200_0_5_mod_7
• unitcommit_200_100_1_mod_8
• unitcommit_200_100_2_mod_7
• unitcommit_200_100_2_mod_8
• unitcommit_50_20_2_mod_8

• wastepaper3
  
  Layout-Optimization of Screening Systems in Recovered Paper Production - 3 Screens
• wastepaper4
  
  Layout-Optimization of Screening Systems in Recovered Paper Production - 4 Screens
• wastepaper5
  
  Layout-Optimization of Screening Systems in Recovered Paper Production- 5 Screens
• wastepaper6
  
  Layout-Optimization of Screening Systems in Recovered Paper Production - 6 Screens

• waste
• wastewater02m1
• wastewater02m2
• wastewater04m1
• wastewater04m2
• wastewater05m1
• wastewater05m2
• wastewater11m1
• wastewater11m2
• wastewater12m1
• wastewater12m2
• wastewater13m1
• wastewater13m2
• wastewater14m1
• wastewater14m2
• wastewater15m1
• wastewater15m2

• watercontamination0202
  
  Inverse problem for the determination of contamination sources in municipal water networks. The constraints come from discretized dynamic models of the water network quality model. The objective is the least squares error between calculated and measured network concentrations with a regularization term to force a unique solution. Integer variables are added to restrict the allowable contamination scenarios.
• watercontamination0202r
  
  Inverse problem for the determination of contamination sources in municipal water networks. The constraints come from discretized dynamic models of the water network quality model. The objective is the least squares error between calculated and measured network concentrations with a regularization term to force a unique solution. Integer variables are added to restrict the allowable contamination scenarios.
• watercontamination0303
  
  Inverse problem for the determination of contamination sources in municipal water networks. The constraints come from discretized dynamic models of the water network quality model. The objective is the least squares error between calculated and measured network concentrations with a regularization term to force a unique solution. Integer variables are added to restrict the allowable contamination scenarios.
• watercontamination0303r
  
  Inverse problem for the determination of contamination sources in municipal water networks. The constraints come from discretized dynamic models of the water network quality model. The objective is the least squares error between calculated and measured network concentrations with a regularization term to force a unique solution. Integer variables are added to restrict the allowable contamination scenarios.

• water
• water3
• water4
• waterful2
• waternd1
• waternd2
• waternd_blacksburg
• waternd_fossiron
• waternd_fosspoly0
• waternd_fosspoly1
• waternd_hanoi
• waternd_modena
• waternd_pescara
• waternd_shamir
• waters
• watersbp
• watersym1
• watersym2
• watertreatnd_conc
  
  Concentration Based Approach
• watertreatnd_flow
  
  Flow Based Approach
• waterund01
• waterund08
• waterund11
• waterund14
• waterund17
• waterund18
• waterund22
• waterund25
• waterund27
• waterund28
• waterund32
• waterund36
• waterx
• waterz

• waterno1_01
• waterno1_02
• waterno1_03
• waterno1_04
• waterno1_06
• waterno1_09
• waterno1_12
• waterno1_18
• waterno1_24
• waterno2_01
• waterno2_02
• waterno2_03
• waterno2_04
• waterno2_06
• waterno2_09
• waterno2_12
• waterno2_18
• waterno2_24

• lakes

• windfac