MINLPLib

A Library of Mixed-Integer and Continuous Nonlinear Programming Instances

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Instance process

Formats ams gms mod nl osil py
Primal Bounds (infeas ≤ 1e-08)
-1161.33660200 p1 ( gdx sol )
(infeas: 5e-13)
Other points (infeas > 1e-08)  
Dual Bounds
-1161.33660500 (ANTIGONE)
-1161.33665800 (BARON)
-1161.33660300 (COUENNE)
-1161.33692900 (LINDO)
-1161.33661900 (SCIP)
References Bracken, Jerome and McCormick, Garth P, Chapter 4. In Bracken, Jerome and McCormick, Garth P, Selected Applications of Nonlinear Programming, John Wiley and Sons, New York, 1968.
Source GAMS Model Library model process
Application Alkylation
Added to library 31 Jul 2001
Problem type NLP
#Variables 10
#Binary Variables 0
#Integer Variables 0
#Nonlinear Variables 8
#Nonlinear Binary Variables 0
#Nonlinear Integer Variables 0
Objective Sense min
Objective type quadratic
Objective curvature indefinite
#Nonzeros in Objective 6
#Nonlinear Nonzeros in Objective 2
#Constraints 7
#Linear Constraints 3
#Quadratic Constraints 1
#Polynomial Constraints 1
#Signomial Constraints 1
#General Nonlinear Constraints 1
Operands in Gen. Nonlin. Functions div mul
Constraints curvature indefinite
#Nonzeros in Jacobian 21
#Nonlinear Nonzeros in Jacobian 9
#Nonzeros in (Upper-Left) Hessian of Lagrangian 17
#Nonzeros in Diagonal of Hessian of Lagrangian 3
#Blocks in Hessian of Lagrangian 2
Minimal blocksize in Hessian of Lagrangian 4
Maximal blocksize in Hessian of Lagrangian 4
Average blocksize in Hessian of Lagrangian 4.0
#Semicontinuities 0
#Nonlinear Semicontinuities 0
#SOS type 1 0
#SOS type 2 0
Minimal coefficient 1.0000e-03
Maximal coefficient 9.8000e+01
Infeasibility of initial point 1.224
Sparsity Jacobian Sparsity of Objective Gradient and Jacobian
Sparsity Hessian of Lagrangian Sparsity of Hessian of Lagrangian

$offlisting
*  
*  Equation counts
*      Total        E        G        L        N        X        C        B
*          8        8        0        0        0        0        0        0
*  
*  Variable counts
*                   x        b        i      s1s      s2s       sc       si
*      Total     cont   binary  integer     sos1     sos2    scont     sint
*         11       11        0        0        0        0        0        0
*  FX      0
*  
*  Nonzero counts
*      Total    const       NL      DLL
*         28       17       11        0
*
*  Solve m using NLP minimizing objvar;


Variables  x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,objvar;

Positive Variables  x2,x3,x4,x5;

Equations  e1,e2,e3,e4,e5,e6,e7,e8;


e1.. -(1.12 + 0.13167*x8 - 0.00667*sqr(x8))*x1 + x4 =E= 0;

e2..  - x1 + 1.22*x4 - x5 =E= 0;

e3.. -0.001*x4*x9*x6/(98 - x6) + x3 =E= 0;

e4.. -(1.098*x8 - 0.038*sqr(x8)) - 0.325*x6 + x7 =E= 57.425;

e5.. -(x2 + x5)/x1 + x8 =E= 0;

e6..    x9 + 0.222*x10 =E= 35.82;

e7..  - 3*x7 + x10 =E= -133;

e8.. -0.063*x4*x7 + 5.04*x1 + 0.035*x2 + 10*x3 + 3.36*x5 - objvar =E= 0;

* set non-default bounds
x1.lo = 10; x1.up = 2000;
x2.up = 16000;
x3.up = 120;
x4.up = 5000;
x5.up = 2000;
x6.lo = 85; x6.up = 93;
x7.lo = 90; x7.up = 95;
x8.lo = 3; x8.up = 12;
x9.lo = 1.2; x9.up = 4;
x10.lo = 145; x10.up = 162;

* set non-default levels
x1.l = 1745;
x2.l = 12000;
x3.l = 110;
x4.l = 3048;
x5.l = 1974;
x6.l = 89.2;
x7.l = 92.8;
x8.l = 8;
x9.l = 3.6;
objvar.l = -872;

Model m / all /;

m.limrow=0; m.limcol=0;
m.tolproj=0.0;

$if NOT '%gams.u1%' == '' $include '%gams.u1%'

$if not set NLP $set NLP NLP
Solve m using %NLP% minimizing objvar;


Last updated: 2024-12-17 Git hash: 8eaceb91
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