I write some mistake in the file table.math
The correct pseudo code is
set I;
set J;
param ik;
param jk;
param L;
param U;
param borne;
param a{i in I, j in J};
var y{i in I, j in J} >=0;
var x{i in I, j in J} >=0,integer;
/* model */
minimize problem:sum{i in I, j in J} x[i,j];
s.t. x[ik,jk]=1;
s.t. maximize contraint: y[ik,jk] >= a[ik,jk]+borne; 'It is a constraint
which is the linear problem' Is it possible ?
s.t. Ligne{i in I}: sum{j in J} y[i,j] = a[i,0];
s.t. Colonne{j in J}: sum{i in I} y[i,j] = a[0,j];
s.t. Total: sum{i in I,j in J} y[i,j]=a[0,0];
s.t. Borneinf{i in I, j in J}: y[i,j]>=a[i,j]-L*x[i,j];
s.t. Bornesup{i in I, j in J}: y[i,j]<=a[i,j]+U*x[i,j];
end;
Alexandre DEPIRE
I give you the pseudo-code of my problem.
My goal is to find the optimal values for x.
First the file table.data
data;
set I := 0 1 2 3 4;
set J := 0 1 2 3;
param a: 0 1 2 3 :=
0 71 31 22 18
1 25 7 10 8
2 26 12 9 5
3 13 7 3 3
4 7 5 0 2;
param ik :=4;
param jk :=3;
param L:=0;
param U:=71;
param borne:=2;
end;
and the file table.math
set I;
set J;
param ik;
param jk;
param L;
param U;
param borne;
param a{i in I, j in J};
param x{i in I, j in J};
var y{i in I, j in J} >=0;
var x{i in I, j in J} >=0,integer;
/* model */
maximize problem:sum{i in I, j in J} x[i,j];
s.t. x[ik,jk]=1;
maximize contraint: y[ik,jk] >= a[ik,jk]+borne; 'It is a
constraint which is the linear problem' Is it possible ?
s.t. Ligne{i in I}: sum{j in J} y[i,j] = a[i,0];
s.t. Colonne{j in J}: sum{i in I} y[i,j] = a[0,j];
s.t. Total: sum{i in I,j in J} y[i,j]=a[0,0];
s.t. Borneinf{i in I, j in J}: y[i,j]>=a[i,j]-L*x[i,j];
s.t. Bornesup{i in I, j in J}: y[i,j]<=a[i,j]+U*x[i,j];
end;
I hope that i don't make mistakes in the code, and that the problem is
well posed.
Alexandre DEPIRE
> I would like to know if the following
> problem could be done with GLPK or any software (free) ?
> Let x(i,j) in {0,1} for i in I, and
> j in J, unknown variable
> Let y(i,j) in R, for i in I, j in J,
> unknown variable
> Let a(i,j), l(i,j) and u(i,j) known
> data
> Let K an subset of IxJ, K={(ik,jk)}
> known
> Problem:
> Min sum( x( i , j ) )
> subject
> to
> $
> x( ik , jk )=1
> /* some x(i,j)=1, the
> others could be found */
> $
> max y( ik , jk ) >= a( ik , jk ) + U*x(ik,jk)
>
> subject to
>
> linear constraint on y(i,j)
>
> a(i,j)-l(i,j)*x(i,j) <= y(i,j) <= a(i,j)+u(i,j)*x(i,j)
You may write your model in GNU MathProg, a modeling language supported
by glpk, and then solve it with glpsol, the glpk stand-alone solver.
For more details please see the glpk documentation included in the
distribution tarball.
_______________________________________________
Help-glpk mailing list
[email protected]
http://lists.gnu.org/mailman/listinfo/help-glpk
_______________________________________________
Help-glpk mailing list
[email protected]
http://lists.gnu.org/mailman/listinfo/help-glpk
