/**
* @param basicMatrixIn the coefficient matrix of constraints
* @return Array[2][m+1]
* First row is just reduced cost
* Second row is dual cost vector
*/
private static double[][] calLP(double[][] basicMatrixIn, double[] orderNumVec)
{
double[][] basicMatrix = cloneMatrix(basicMatrixIn); // LP solve modifies our input array
int nVar = basicMatrix.Length;
IntPtr solver = Lpsolve.make_lp(0, nVar);
Lpsolve.set_verbose(solver, 1);
// add constraints
for (int r = 0; r < nVar; r++)
{
Lpsolve.add_constraint(solver, basicMatrix[r], Lpsolve.lpsolve_constr_types.EQ, orderNumVec[r + 1]);
}
// set objective function
double[] minCoef = new double[nVar + 1]; // coefficients
for (int i = 0; i < minCoef.Length; i++)
{
minCoef[i] = 1;
}
minCoef[0] = nVar;
Lpsolve.set_obj_fn(solver, minCoef);
Lpsolve.set_minim(solver);
// solve the problem
int solFlag = (int)Lpsolve.solve(solver);
// solution
double[] rhs = new double[Lpsolve.get_Ncolumns(solver)];
Lpsolve.get_variables(solver, rhs);
double[] duals = new double[Lpsolve.get_Ncolumns(solver) + Lpsolve.get_Nrows(solver) + 1];
Lpsolve.get_dual_solution(solver, duals);
// delete the problem and free memory
Lpsolve.delete_lp(solver);
// Prepare result
double[][] rst = new double[3][];
rst[0] = new double[nVar + 1];
rst[0][0] = solFlag;
rst[1] = new double[nVar + 1];
rst[1][0] = nVar;
rhs.CopyTo(rst[1], 1);
rst[2] = new double[nVar + 1];
rst[2][0] = nVar;
Array.Copy(duals, 1, rst[2], 1, nVar);
return(rst);
}
