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Instance mathopt2

Formatsⓘ ams gms mod nl osil pip py
Primal Bounds (infeas ≤ 1e-08)ⓘ
0. p1 ( gdx sol )
(infeas: 4e-17)
Other points (infeas > 1e-08)ⓘ  
Dual Boundsⓘ
-0. (ANTIGONE)
-0. (BARON)
0. (COUENNE)
0. (LINDO)
0. (SCIP)
Referencesⓘ Mathematica, MathOptimizer - An Advanced Modeling and Optimization System for Mathematica Users.
Pinter, J D, Global Optimization in Action - Continuous and Lipschitz Optimization: Algorithms, Implementations, and Applications, Kluwer Acadameic Publishers, 1996.
Pinter, J D, Computational Global Optimization in Nonlinear Systems - An Interactive Tutorial, Lionheart Publishing, Atlanta, GA, 2001.
Sourceⓘ GAMS Model Library model mathopt2
Applicationⓘ Test Problem
Added to libraryⓘ 31 Jul 2001
Problem typeⓘ NLP
#Variablesⓘ 2
#Binary Variablesⓘ 0
#Integer Variablesⓘ 0
#Nonlinear Variablesⓘ 2
#Nonlinear Binary Variablesⓘ 0
#Nonlinear Integer Variablesⓘ 0
Objective Senseⓘ min
Objective typeⓘ polynomial
Objective curvatureⓘ nonconcave
#Nonzeros in Objectiveⓘ 2
#Nonlinear Nonzeros in Objectiveⓘ 2
#Constraintsⓘ 4
#Linear Constraintsⓘ 3
#Quadratic Constraintsⓘ 1
#Polynomial Constraintsⓘ 0
#Signomial Constraintsⓘ 0
#General Nonlinear Constraintsⓘ 0
Operands in Gen. Nonlin. Functionsⓘ  
Constraints curvatureⓘ indefinite
#Nonzeros in Jacobianⓘ 8
#Nonlinear Nonzeros in Jacobianⓘ 2
#Nonzeros in (Upper-Left) Hessian of Lagrangianⓘ 4
#Nonzeros in Diagonal of Hessian of Lagrangianⓘ 2
#Blocks in Hessian of Lagrangianⓘ 1
Minimal blocksize in Hessian of Lagrangianⓘ 2
Maximal blocksize in Hessian of Lagrangianⓘ 2
Average blocksize in Hessian of Lagrangianⓘ 2.0
#Semicontinuitiesⓘ 0
#Nonlinear Semicontinuitiesⓘ 0
#SOS type 1ⓘ 0
#SOS type 2ⓘ 0
Minimal coefficientⓘ 1.0000e+00
Maximal coefficientⓘ 1.0000e+01
Infeasibility of initial pointⓘ 210
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
*          5        3        0        2        0        0        0        0
*  
*  Variable counts
*                   x        b        i      s1s      s2s       sc       si
*      Total     cont   binary  integer     sos1     sos2    scont     sint
*          3        3        0        0        0        0        0        0
*  FX      0
*  
*  Nonzero counts
*      Total    const       NL      DLL
*         11        7        4        0
*
*  Solve m using NLP minimizing objvar;


Variables  x1,x2,objvar;

Equations  e1,e2,e3,e4,e5;


e1.. -(sqr(2*sqr(x1) - x2) + sqr(x2 - 6*sqr(x1))) + objvar =E= 0;

e2.. x1 - (x1*x2 + 10*x2) =E= 0;

e3..    x1 - 3*x2 =E= 0;

e4..    x1 + x2 =L= 1;

e5..  - x1 + x2 =L= 2;

* set non-default levels
x1.l = 10;
x2.l = -10;

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;


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