MINLPLib

A Library of Mixed-Integer and Continuous Nonlinear Programming Instances

Home // Instances // Documentation // Download // Statistics


Instance ex9_2_5

Formats ams gms lp mod nl osil pip py
Primal Bounds (infeas ≤ 1e-08)
9.00000000 p1 ( gdx sol )
(infeas: 2e-15)
5.00000015 p2 ( gdx sol )
(infeas: 9e-16)
Other points (infeas > 1e-08)  
Dual Bounds
-0.00000048 (ANTIGONE)
4.99999999 (BARON)
5.00000000 (COUENNE)
5.00000000 (GUROBI)
5.00000000 (LINDO)
5.00000000 (SCIP)
References Floudas, C A, Pardalos, Panos M, Adjiman, C S, Esposito, W R, Gumus, Zeynep H, Harding, S T, Klepeis, John L, Meyer, Clifford A, and Schweiger, C A, Handbook of Test Problems in Local and Global Optimization, Kluwer Academic Publishers, 1999.
Clark, P A and Westerberg, A W, Bilevel Programming for Steady-State Chemical Process Design - I. Fundamentals and Algorithms, Computers and Chemical Engineering, 14:1, 1990, 87-97.
Source Test Problem ex9.2.5 of Chapter 9 of Floudas e.a. handbook
Added to library 31 Jul 2001
Problem type QCQP
#Variables 8
#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 convex
#Nonzeros in Objective 2
#Nonlinear Nonzeros in Objective 2
#Constraints 7
#Linear Constraints 4
#Quadratic Constraints 3
#Polynomial Constraints 0
#Signomial Constraints 0
#General Nonlinear Constraints 0
Operands in Gen. Nonlin. Functions  
Constraints curvature indefinite
#Nonzeros in Jacobian 19
#Nonlinear Nonzeros in Jacobian 6
#Nonzeros in (Upper-Left) Hessian of Lagrangian 8
#Nonzeros in Diagonal of Hessian of Lagrangian 2
#Blocks in Hessian of Lagrangian 5
Minimal blocksize in Hessian of Lagrangian 1
Maximal blocksize in Hessian of Lagrangian 2
Average blocksize in Hessian of Lagrangian 1.6
#Semicontinuities 0
#Nonlinear Semicontinuities 0
#SOS type 1 0
#SOS type 2 0
Minimal coefficient 1.0000e+00
Maximal coefficient 3.0000e+00
Infeasibility of initial point 14
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
*          9        9        0        0        0        0        0        0
*  FX      0
*  
*  Nonzero counts
*      Total    const       NL      DLL
*         22       14        8        0
*
*  Solve m using NLP minimizing objvar;


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

Positive Variables  x3,x4,x5,x6,x7,x8,x9;

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


e1.. (-3 + x3)*(-3 + x3) + (-2 + x1)*(-2 + x1) - objvar =E= 0;

e2..    x1 - 2*x3 + x4 =E= 1;

e3..  - 2*x1 + x3 + x5 =E= 2;

e4..    2*x1 + x3 + x6 =E= 14;

e5.. x4*x7 =E= 0;

e6.. x5*x8 =E= 0;

e7.. x6*x9 =E= 0;

e8..    2*x1 + x7 - 2*x8 + 2*x9 =E= 10;

* set non-default bounds
x3.up = 8;

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: 2024-04-02 Git hash: 1dd5fb9b
Imprint / Privacy Policy / License: CC-BY 4.0