MINLPLib

A Library of Mixed-Integer and Continuous Nonlinear Programming Instances

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Instance: nvs04

Formats ams gms mod nl osil pip
Primal Bounds (infeas ≤ 1e-08)
0.72000000 p1 ( gdx sol )
(infeas: 0)
Other points (infeas > 1e-08)  
Dual Bounds
0.72000000 (ANTIGONE)
0.72000000 (BARON)
0.72000000 (COUENNE)
0.72000000 (LINDO)
0.72000000 (SCIP)
References Gupta, Omprakash K and Ravindran, A, Branch and Bound Experiments in Convex Nonlinear Integer Programming, Management Science, 13:12, 1985, 1533-1546.
Tawarmalani, M and Sahinidis, N V, Exact Algorithms for Global Optimization of Mixed-Integer Nonlinear Programs. In Pardalos, Panos M and Romeijn, H Edwin, Eds, Handbook of Global Optimization - Volume 2: Heuristic Approaches, Kluwer Academic Publishers, 65-85.
Tawarmalani, M and Sahinidis, N V, Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications, Kluwer, 2002.
Source BARON book instance gupta/gupta04
Added to library 25 Jul 2002
Problem type INLP
#Variables 2
#Binary Variables 0
#Integer Variables 2
#Nonlinear Variables 2
#Nonlinear Binary Variables 0
#Nonlinear Integer Variables 2
Objective Sense min
Objective type polynomial
Objective curvature nonconcave
#Nonzeros in Objective 2
#Nonlinear Nonzeros in Objective 2
#Constraints 0
#Linear Constraints 0
#Quadratic Constraints 0
#Polynomial Constraints 0
#Signomial Constraints 0
#General Nonlinear Constraints 0
Operands in Gen. Nonlin. Functions  
Constraints curvature linear
#Nonzeros in Jacobian 0
#Nonlinear Nonzeros in Jacobian 0
#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
Infeasibility of initial point 0
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
*          1        1        0        0        0        0        0        0
*  
*  Variable counts
*                   x        b        i      s1s      s2s       sc       si
*      Total     cont   binary  integer     sos1     sos2    scont     sint
*          3        1        0        2        0        0        0        0
*  FX      0
*  
*  Nonzero counts
*      Total    const       NL      DLL
*          3        1        2        0
*
*  Solve m using MINLP minimizing objvar;


Variables  i1,i2,objvar;

Integer Variables  i1,i2;

Equations  e1;


e1.. -(100*sqr(0.5 - sqr(0.6 + i1) + i2) + sqr(0.4 - i1)) + objvar =E= 0;

* set non-default bounds
i1.up = 200;
i2.up = 200;

* set non-default levels
i1.l = 100;
i2.l = 100;

Model m / all /;

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

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

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


Last updated: 2019-09-10 Git hash: 87a8f78d
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