private static int Demo()
{
// We will build the model row by row
// So we start with creating a model with 0 rows and 2 columns
int Ncol = 2; // there are two variables in the model
using (LpSolve lp = LpSolve.make_lp(0, Ncol))
{
if (lp == null)
{
return(1); // couldn't construct a new model...
}
//let us name our variables. Not required, but can be useful for debugging
lp.set_col_name(1, "x");
lp.set_col_name(2, "y");
//create space large enough for one row
int[] colno = new int[Ncol];
double[] row = new double[Ncol];
// makes building the model faster if it is done rows by row
lp.set_add_rowmode(true);
int j = 0;
//construct first row (120 x + 210 y <= 15000)
colno[j] = 1; // first column
row[j++] = 120;
colno[j] = 2; // second column
row[j++] = 210;
// add the row to lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.LE, 15000) == false)
{
return(3);
}
//construct second row (110 x + 30 y <= 4000)
j = 0;
colno[j] = 1; // first column
row[j++] = 110;
colno[j] = 2; // second column
row[j++] = 30;
// add the row to lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.LE, 4000) == false)
{
return(3);
}
//construct third row (x + y <= 75)
j = 0;
colno[j] = 1; // first column
row[j++] = 1;
colno[j] = 2; // second column
row[j++] = 1;
// add the row to lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.LE, 75) == false)
{
return(3);
}
//rowmode should be turned off again when done building the model
lp.set_add_rowmode(false);
//set the objective function (143 x + 60 y)
j = 0;
colno[j] = 1; // first column
row[j++] = 143;
colno[j] = 2; // second column
row[j++] = 60;
if (lp.set_obj_fnex(j, row, colno) == false)
{
return(4);
}
// set the object direction to maximize
lp.set_maxim();
// just out of curioucity, now show the model in lp format on screen
// this only works if this is a console application. If not, use write_lp and a filename
lp.write_lp("model.lp");
// I only want to see important messages on screen while solving
lp.set_verbose(3);
// Now let lpsolve calculate a solution
lpsolve_return s = lp.solve();
if (s != lpsolve_return.OPTIMAL)
{
return(5);
}
// a solution is calculated, now lets get some results
// objective value
Debug.WriteLine("Objective value: " + lp.get_objective());
// variable values
lp.get_variables(row);
for (j = 0; j < Ncol; j++)
{
Debug.WriteLine(lp.get_col_name(j + 1) + ": " + row[j]);
}
}
return(0);
} //Demo
private static int zadanie8()
{
int Ncol = 3; // trzy zmienne w modelu
using (LpSolve lp = LpSolve.make_lp(0, Ncol))
{
if (lp == null)
{
return(1); // jesli nie moglo zbudowac modelu...
}
//nazwanie zmiennych
lp.set_col_name(1, "W1");
lp.set_col_name(2, "W2");
lp.set_col_name(3, "W3");
//przestrzen tablicowa do obliczen
int[] colno = new int[Ncol];
double[] row = new double[Ncol];
lp.set_add_rowmode(true);
int j = 0;
/////////////////////////////////////// rownanie pierwsze
j = 0;
colno[j] = 1;
row[j++] = 1.5;
colno[j] = 2;
row[j++] = 3;
colno[j] = 3;
row[j++] = 4;
// dodanie rzedu do lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.LE, 1500) == false)
{
return(3);
}
/////////////////////////////////////////////////////////////////////// rownanie drugei
j = 0;
colno[j] = 1;
row[j++] = 3;
colno[j] = 2;
row[j++] = 2;
colno[j] = 3;
row[j++] = 1;
// dodanie rzedu do lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.LE, 1200) == false)
{
return(3);
}
lp.set_add_rowmode(false);
//funkcja celu
j = 0;
colno[j] = 1;
row[j++] = 12;
colno[j] = 2;
row[j++] = 18;
colno[j] = 3;
row[j++] = 12;
if (lp.set_obj_fnex(j, row, colno) == false)
{
return(4);
}
// szukanie maksa
lp.set_maxim();
lp.write_lp("model.lp");
lp.set_verbose(3);
lpsolve_return s = lp.solve();
if (s != lpsolve_return.OPTIMAL)
{
return(5);
}
Debug.WriteLine("Objective value: " + lp.get_objective());
lp.get_variables(row);
for (j = 0; j < Ncol; j++)
{
Debug.WriteLine(lp.get_col_name(j + 1) + ": " + row[j]);
}
}
return(0);
}
private static int SolveA()
{
int lKolumn = 2; // trzy zmienne w modelu
using (LpSolve lp = LpSolve.make_lp(0, lKolumn))
{
if (lp == null)
{
return(1); // jesli nie moglo zbudowac modelu...
}
//nazwanie zmiennych
lp.set_col_name(1, "P1");
lp.set_col_name(2, "P2");
//przestrzen tablicowa do obliczen
int[] colno = new int[lKolumn];
double[] row = new double[lKolumn];
lp.set_add_rowmode(true);
int j = 0;
// rownanie pierwsze
j = 0;
colno[j] = 1;
row[j++] = 3;
colno[j] = 2;
row[j++] = 9;
// dodanie rzedu do lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.GE, 27) == false)
{
return(3);
}
// rownanie drugie
j = 0;
colno[j] = 1;
row[j++] = 8;
colno[j] = 2;
row[j++] = 4;
// dodanie rzedu do lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.GE, 32) == false)
{
return(3);
}
// rownanie trzecie
j = 0;
colno[j] = 1;
row[j++] = 12;
colno[j] = 2;
row[j++] = 3;
// dodanie rzedu do lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.GE, 36) == false)
{
return(3);
}
lp.set_add_rowmode(false);
//funkcja celu
j = 0;
colno[j] = 1;
row[j++] = 6;
colno[j] = 2;
row[j++] = 9;
if (lp.set_obj_fnex(j, row, colno) == false)
{
return(4);
}
// szukanie minimum
lp.set_minim();
lp.write_lp("model.lp");
lp.set_verbose(3);
lpsolve_return s = lp.solve();
if (s != lpsolve_return.OPTIMAL)
{
return(5);
}
Debug.WriteLine("Objective value: " + lp.get_objective());
lp.get_variables(row);
for (j = 0; j < lKolumn; j++)
{
Debug.WriteLine(lp.get_col_name(j + 1) + ": " + row[j]);
}
}
return(0);
}
public int SolveLinearForCol()
{
int Ncol = _matrix.GetLength(1);//5 columns
int Nrow = _matrix.GetLength(0);
using (LpSolve lp = LpSolve.make_lp(Nrow, Ncol))
{
if (lp == null)
{
return(1); // couldn't construct a new model...
}
lp.set_col_name(1, "x1");
lp.set_col_name(2, "x2");
lp.set_col_name(3, "x3");
lp.set_col_name(4, "x4");
lp.set_col_name(5, "x5");
//create space large enough for one row
int[] colno = new int[Ncol];
double[] row = new double[Ncol];
// makes building the model faster if it is done rows by row
lp.set_add_rowmode(true);
// 4 10 16 14 17
// 5 4 2 16 14
// 17 3 6 10 15
// 14 16 18 4 7
// 6 3 10 18 15
//construct first row (4x1 + 5x2 +176x3 + 14x4 + 6x5 <= -1)
int j = 0;
colno[j] = 1; // first column
row[j++] = 4;
colno[j] = 2; // second column
row[j++] = 5;
colno[j] = 3; // second column
row[j++] = 17;
colno[j] = 4; // second column
row[j++] = 14;
colno[j] = 5; // second column
row[j++] = 6;
// add the row to lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.GE, 1) == false)
{
return(3);
}
//construct 2 row (10x1 + 4x2 +3x3 + 16x4 + 3x5 <= -1)
j = 0;
colno[j] = 1; // first column
row[j++] = 10;
colno[j] = 2; // second column
row[j++] = 4;
colno[j] = 3; // second column
row[j++] = 3;
colno[j] = 4; // second column
row[j++] = 16;
colno[j] = 5; // second column
row[j++] = 3;
// add the row to lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.GE, 1) == false)
{
return(3);
}
//construct 3 row (16x1 + 2x2 +6x3 + 18x4 + 10x5 <= -1)
j = 0;
colno[j] = 1; // first column
row[j++] = 16;
colno[j] = 2; // second column
row[j++] = 2;
colno[j] = 3; // second column
row[j++] = 6;
colno[j] = 4; // second column
row[j++] = 18;
colno[j] = 5; // second column
row[j++] = 10;
// add the row to lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.GE, 1) == false)
{
return(3);
}
//construct 4 row (14x1 + 16x2 +10x3 + 4x4 + 18x5 <= -1)
j = 0;
colno[j] = 1; // first column
row[j++] = 14;
colno[j] = 2; // second column
row[j++] = 16;
colno[j] = 3; // second column
row[j++] = 10;
colno[j] = 4; // second column
row[j++] = 4;
colno[j] = 5; // second column
row[j++] = 18;
// add the row to lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.GE, 1) == false)
{
return(3);
}
//construct 5 row (17x1 + 14x2 +15x3 + 7x4 + 15x5 <= -1)
j = 0;
colno[j] = 1; // first column
row[j++] = 17;
colno[j] = 2; // second column
row[j++] = 14;
colno[j] = 3; // second column
row[j++] = 15;
colno[j] = 4; // second column
row[j++] = 7;
colno[j] = 5; // second column
row[j++] = 15;
// add the row to lpsolve
if (lp.add_constraintex(j, row, colno, lpsolve_constr_types.GE, 1) == false)
{
return(3);
}
lp.set_add_rowmode(false);
//set the objective function (1 x + 1 x2 +x3+x4+x5)
j = 0;
colno[j] = 1; // first column
row[j++] = 1;
colno[j] = 2; // second column
row[j++] = 1;
colno[j] = 3; // 3 column
row[j++] = 1;
colno[j] = 4; // 4 column
row[j++] = 1;
colno[j] = 5; // 5 column
row[j++] = 1;
if (lp.set_obj_fnex(j, row, colno) == false)
{
return(4);
}
// set the object direction to maximize
lp.set_minim();
// just out of curioucity, now show the model in lp format on screen
// this only works if this is a console application. If not, use write_lp and a filename
lp.write_lp("model2.lp");
// I only want to see important messages on screen while solving
lp.set_verbose(lpsolve_verbosity.IMPORTANT);
// Now let lpsolve calculate a solution
lpsolve_return s = lp.solve();
if (s != lpsolve_return.OPTIMAL)
{
return(5);
}
// a solution is calculated, now lets get some results
// objective value
Debug.WriteLine("Objective value: " + lp.get_objective());
// variable values
lp.get_variables(row);
for (j = 0; j < Ncol; j++)
{
Console.WriteLine(lp.get_col_name(j + 1) + ": " + row[j]);
}
}
return(1);
}
