MINLPLib
A Library of Mixed-Integer and Continuous Nonlinear Programming Instances
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Instance 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.
Formatsⓘ | ams gms mod nl osil py |
Primal Bounds (infeas ≤ 1e-08)ⓘ | |
Other points (infeas > 1e-08)ⓘ | |
Dual Boundsⓘ | 0.00000000 (ANTIGONE) 0.00000000 (BARON) 0.00000000 (COUENNE) 5.54091471 (LINDO) -99999999809999994880.00000000 (SCIP) |
Referencesⓘ | Bracken, Jerome and McCormick, Garth P, Chapter 6. In Bracken, Jerome and McCormick, Garth P, Selected Applications of Nonlinear Programming, John Wiley and Sons, New York, 1968. |
Sourceⓘ | GAMS Model Library model ship |
Applicationⓘ | Structural Optimization |
Added to libraryⓘ | 15 Aug 2014 |
Problem typeⓘ | NLP |
#Variablesⓘ | 10 |
#Binary Variablesⓘ | 0 |
#Integer Variablesⓘ | 0 |
#Nonlinear Variablesⓘ | 10 |
#Nonlinear Binary Variablesⓘ | 0 |
#Nonlinear Integer Variablesⓘ | 0 |
Objective Senseⓘ | min |
Objective typeⓘ | signomial |
Objective curvatureⓘ | indefinite |
#Nonzeros in Objectiveⓘ | 6 |
#Nonlinear Nonzeros in Objectiveⓘ | 6 |
#Constraintsⓘ | 17 |
#Linear Constraintsⓘ | 10 |
#Quadratic Constraintsⓘ | 0 |
#Polynomial Constraintsⓘ | 3 |
#Signomial Constraintsⓘ | 3 |
#General Nonlinear Constraintsⓘ | 1 |
Operands in Gen. Nonlin. Functionsⓘ | mul sqrt |
Constraints curvatureⓘ | indefinite |
#Nonzeros in Jacobianⓘ | 48 |
#Nonlinear Nonzeros in Jacobianⓘ | 23 |
#Nonzeros in (Upper-Left) Hessian of Lagrangianⓘ | 41 |
#Nonzeros in Diagonal of Hessian of Lagrangianⓘ | 3 |
#Blocks in Hessian of Lagrangianⓘ | 1 |
Minimal blocksize in Hessian of Lagrangianⓘ | 10 |
Maximal blocksize in Hessian of Lagrangianⓘ | 10 |
Average blocksize in Hessian of Lagrangianⓘ | 10.0 |
#Semicontinuitiesⓘ | 0 |
#Nonlinear Semicontinuitiesⓘ | 0 |
#SOS type 1ⓘ | 0 |
#SOS type 2ⓘ | 0 |
Minimal coefficientⓘ | 3.9000e-03 |
Maximal coefficientⓘ | 4.9500e+02 |
Infeasibility of initial pointⓘ | 407.3 |
Sparsity Jacobianⓘ | |
Sparsity Hessian of Lagrangianⓘ |
$offlisting * * Equation counts * Total E G L N X C B * 18 5 13 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 * 55 26 29 0 * * Solve m using NLP minimizing objvar; Variables x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,objvar; Equations e1,e2,e3,e4,e5,e6,e7,e8,e9,e10,e11,e12,e13,e14,e15,e16,e17,e18; e1.. -0.5*x9*x4*(0.8*x7 + 0.333333333333333*x8) + x1 =E= 0; e2.. -0.5*x9*x5*(0.8*x7 + 0.333333333333333*x8) + x2 =E= 0; e3.. -0.5*x9*x6*(0.8*x7 + 0.333333333333333*x8) + x3 =E= 0; e4.. -sqrt(x8*x8 - x9*x9) - x7 + x10 =E= 0; e5.. x1 - 8.4652734375*x10 =G= 0; e6.. x2 - 9.65006510416667*x10 =G= 0; e7.. x3 - 8.8716796875*x10 =G= 0; e8.. 0.5*x1*x9 - 2.2*(8.4652734375*x10)**1.33333333333333 =G= 0; e9.. 0.5*x2*x9 - 2.2*(9.65006510416667*x10)**1.33333333333333 =G= 0; e10.. 0.5*x3*x9 - 2.2*(8.8716796875*x10)**1.33333333333333 =G= 0; e11.. x4 - 0.0111771747883801*x7 =G= 0.2; e12.. x5 - 0.0137655360411427*x7 =G= 0.2; e13.. x6 - 0.0155663872253648*x7 =G= 0.2; e14.. x4 - 0.0111771747883801*x8 =G= 0.2; e15.. x5 - 0.0137655360411427*x8 =G= 0.2; e16.. x6 - 0.0155663872253648*x8 =G= 0.2; e17.. x8 - x9 =G= 0; e18.. -(0.0039*x7 + 0.0039*x8)*(495*x4 + 385*x5 + 315*x6)/x10 + objvar =E= 0; * set non-default bounds x4.lo = 1.05; x5.lo = 1.05; x6.lo = 1.05; x10.lo = 1; * set non-default levels x1.l = 934.032; x2.l = 934.032; x3.l = 1011.868; x4.l = 1.2; x5.l = 1.2; x6.l = 1.3; x7.l = 45.8; x8.l = 43.2; x9.l = 30.5; x10.l = 76.3939536510076; 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