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Instance ex8_5_2
Formatsⓘ | ams gms mod nl osil py |
Primal Bounds (infeas ≤ 1e-08)ⓘ | |
Other points (infeas > 1e-08)ⓘ | |
Dual Boundsⓘ | -1.50601662 (COUENNE) -0.00000001 (LINDO) -0.00001869 (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. |
Sourceⓘ | Test Problem ex8.5.2 of Chapter 8 of Floudas e.a. handbook |
Added to libraryⓘ | 31 Jul 2001 |
Problem typeⓘ | NLP |
#Variablesⓘ | 6 |
#Binary Variablesⓘ | 0 |
#Integer Variablesⓘ | 0 |
#Nonlinear Variablesⓘ | 6 |
#Nonlinear Binary Variablesⓘ | 0 |
#Nonlinear Integer Variablesⓘ | 0 |
Objective Senseⓘ | min |
Objective typeⓘ | nonlinear |
Objective curvatureⓘ | unknown |
#Nonzeros in Objectiveⓘ | 6 |
#Nonlinear Nonzeros in Objectiveⓘ | 6 |
#Constraintsⓘ | 4 |
#Linear Constraintsⓘ | 2 |
#Quadratic Constraintsⓘ | 1 |
#Polynomial Constraintsⓘ | 1 |
#Signomial Constraintsⓘ | 0 |
#General Nonlinear Constraintsⓘ | 0 |
Operands in Gen. Nonlin. Functionsⓘ | div log mul |
Constraints curvatureⓘ | indefinite |
#Nonzeros in Jacobianⓘ | 14 |
#Nonlinear Nonzeros in Jacobianⓘ | 6 |
#Nonzeros in (Upper-Left) Hessian of Lagrangianⓘ | 17 |
#Nonzeros in Diagonal of Hessian of Lagrangianⓘ | 5 |
#Blocks in Hessian of Lagrangianⓘ | 2 |
Minimal blocksize in Hessian of Lagrangianⓘ | 3 |
Maximal blocksize in Hessian of Lagrangianⓘ | 3 |
Average blocksize in Hessian of Lagrangianⓘ | 3.0 |
#Semicontinuitiesⓘ | 0 |
#Nonlinear Semicontinuitiesⓘ | 0 |
#SOS type 1ⓘ | 0 |
#SOS type 2ⓘ | 0 |
Minimal coefficientⓘ | 1.4998e-01 |
Maximal coefficientⓘ | 3.5380e+00 |
Infeasibility of initial pointⓘ | 1 |
Sparsity Jacobianⓘ | |
Sparsity Hessian of Lagrangianⓘ |
$offlisting * * Equation counts * Total E G L N X C B * 5 5 0 0 0 0 0 0 * * Variable counts * x b i s1s s2s sc si * Total cont binary integer sos1 sos2 scont sint * 7 7 0 0 0 0 0 0 * FX 0 * * Nonzero counts * Total const NL DLL * 21 9 12 0 * * Solve m using NLP minimizing objvar; Variables objvar,x2,x3,x4,x5,x6,x7; Equations e1,e2,e3,e4,e5; e1.. -(log(x2)*x2 + log(x3)*x3 + log(x4)*x4 + x7/(x5 - x7) - log(x5 - x7) - 2* x6/x5 + 0.585616681390832*x2 + 3.53797016206289*x3 + 2.18345516206289*x4) + objvar =E= 0; e2.. POWER(x5,3) - sqr(x5)*(1 + x7) + x6*x5 - x6*x7 =E= 0; e3.. -(0.37943*x2*x2 + 0.75885*x2*x3 + 0.48991*x2*x4 + 0.75885*x3*x2 + 0.8836* x3*x3 + 0.23612*x3*x4 + 0.48991*x4*x2 + 0.23612*x4*x3 + 0.63263*x4*x4) + x6 =E= 0; e4.. - 0.14998*x2 - 0.14998*x3 - 0.14998*x4 + x7 =E= 0; e5.. x2 + x3 + x4 =E= 1; * set non-default levels x2.l = 0.333333333333333; x3.l = 0.333333333333333; x4.l = 0.333333333333333; x5.l = 2; x6.l = 1; x7.l = 1; 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: 2024-08-26 Git hash: 6cc1607f