Description

Generates and solves random linear multiplicative models of
"Type 1."  Problem instances are generated as proposed by
Konno and Kuno. Model developed by N. Sahinidis.

Large Model of Type : NLP

Category : GAMS Model library

Main file : lmp1.gms

$title Linear Multiplicative Programs - Type 1 (LMP1,SEQ=251)

$onText
Generates and solves random linear multiplicative models of
"Type 1."  Problem instances are generated as proposed by
Konno and Kuno. Model developed by N. Sahinidis.


H. Konno and T. Kuno, "Linear multiplicative programming",
Mathematical Programming, 56(51-64), 1992.

M. Tawarmalani and N. Sahinidis, Convexification and Global
Optimization in Continuous and Mixed-Integer Nonlinear
Programming: Theory, Algorithms, Software, and Applications,
Kluwer Academic Publishers, 2002.

Keywords: nonlinear programming, linear multiplicative programming, mathematics,
          non-convex quadratic programming, global optimization,
          parametric simplex algorithm
$offText

option optCr = 0, optCa = 1.e-6, limRow = 0, limCol = 0, solPrint = off;

Set
   mm / m1*m220 /
   nn / n1*n200 /
   pp / p1*p5   /;

Set
   m(mm) 'constraints'
   n(nn) 'variables'
   p(pp) 'products'
   c     'cases'     / c1*c10 /
   i     'instances' / i1*i5  /;

* For each case to be solved, we use different (m,n,p) triplets
Table cases(c,*)
          m    n  p
    c1   20   30  2
    c2  120  100  2
    c3  220  200  2
    c4   20   30  3
    c5  120  120  3
    c6  200  180  3
    c7   20   30  4
    c8  100  100  4
    c9  200  200  4
   c10  200  200  5;

Parameter
   cc(pp,nn) 'cost coefficients'
   A(mm,nn)  'constraint coefficients'
   b(mm)     'left-hand-side'
   rep(c,*)  'summary report'
   ResMin
   Resmax
   NodMin
   Nodmax;

Variable
   y(pp)
   x(nn)
   obj;

Equation
   Objective
   Constraints(mm)
   Products(pp);

Objective..      obj  =e= prod(p, y(p));

Products(p)..    y(p) =e= sum(n, cc(p,n)*x(n));

Constraints(m).. b(m) =l= sum(n, A(m,n)*x(n));

x.lo(nn) = 0;

Model lmp1 / all /;

lmp1.workSpace = 32;

rep(c,'AvgResUsd') = 0;
rep(c,'AvgNodUsd') = 0;

loop(c,
   m(mm)  = ord(mm) <= cases(c,'m');
   n(nn)  = ord(nn) <= cases(c,'n');
   p(pp)  = ord(pp) <= cases(c,'p');
   ResMin = inf;
   Resmax = 0;
   NodMin = inf;
   Nodmax = 0;

   loop(i,
      cc(p,n) = uniform(0,100);
      A(m,n)  = uniform(0,100);
      b(m)    = uniform(0,100);

*    Set initial starting point for all models to 0
     x.l(n) = 0;
     y.l(p) = 0;

     solve lmp1 minimizing obj using nlp;

     rep(c,'AvgResUsd') = rep(c,'AvgResUsd') + lmp1.resUsd;
     rep(c,'AvgNodUsd') = rep(c,'AvgNodUsd') + lmp1.nodUsd;
     ResMin = min(ResMin, lmp1.resUsd);
     NodMin = min(NodMin, lmp1.nodUsd);
     ResMax = max(ResMax, lmp1.resUsd);
     NodMax = max(NodMax, lmp1.nodUsd);
   );
   rep(c,'MinResUsd') = ResMin;
   rep(c,'MaxResUsd') = ResMax;
   rep(c,'MinNodUsd') = NodMin;
   rep(c,'MaxNodUsd') = NodMax;
);
rep(c,'AvgResUsd') = rep(c,'AvgResUsd')/card(i);
rep(c,'AvgNodUsd') = rep(c,'AvgNodUsd')/card(i);

display rep;