/********************************************************
  Xpress-BCL C# Example Problems
  ==============================

  file folioheur.cs
  `````````````````
  Modeling a small MIP problem
  to perform portfolio optimization.
   -- Heuristic solution --

  (c) 2008-2024 Fair Isaac Corporation
      authors: S.Heipcke, D.Brett.
********************************************************/

using System;
using System.Text;
using System.IO;
using Optimizer;
using BCL;


namespace Examples
{
    public class TestUGFolioHeur
    {
        const int MAXNUM = 4;          // Max. number of shares to be selected

        const int NSHARES = 10;        // Number of shares
        const int NRISK = 5;           // Number of high-risk shares
        const int NNA = 4;             // Number of North-American shares

	// Estimated return in investment
        double[] RET = {5,17,26,12,8,9,7,6,31,21};

        // High-risk values among shares
        int[] RISK = {1,2,3,8,9};

        // Shares issued in N.-America
        int[] NA = {0,1,2,3};

        // Initialize a new problem in BCL
        XPRBprob p = new XPRBprob("FolioMIPHeur");

        // Fraction of capital used per share
        XPRBvar[] frac = new XPRBvar[NSHARES];

        // 1 if asset is in portfolio, 0 otherwise
        XPRBvar[] buy = new XPRBvar[NSHARES];

        public static void Main()
        {
            XPRB.init();
            int s;
            XPRBexpr Risk,Na,Return,Cap,Num;
            TestUGFolioHeur TestInstance = new TestUGFolioHeur();

            // Create decision variables (including upper bounds for `frac')
            for(s=0;s<NSHARES;s++) TestInstance.frac[s]=TestInstance.p.newVar("frac", TestInstance.buy[s]=TestInstance.p.newVar("buy", Objective: total return Return=new for (s=0; s +=TestInstance.RET[s] Set the objective function Limit percentage of high-risk values Risk=new <=1.0 Minimum amount North-American Na=new>= 0,5);

            // Spend all the capital
            Cap = new XPRBexpr();
            for (s = 0; s < NSHARES; s++)
            	Cap += TestInstance.frac[s];
            TestInstance.p.newCtr(Cap == 1);

            // Limit the total number of assets
            Num = new XPRBexpr();
            for (s = 0; s < NSHARES; s++)
            	Num += TestInstance.buy[s];
            TestInstance.p.newCtr(Num <= MAXNUM);

            // Linking the variables
            for (s = 0; s < NSHARES; s++)
            	TestInstance.p.newCtr(TestInstance.frac[s] <=
            			      TestInstance.buy[s]);

            // Solve problem heuristically
            TestInstance.solveHeur();

            // Solve the problem
            TestInstance.p.setSense(BCLconstant.XPRB_MAXIM);
            TestInstance.p.mipOptimize();              /* Solve the LP-problem */

            // Solution printing
            if (TestInstance.p.getMIPStat() == 4 ||
            	TestInstance.p.getMIPStat() == 6)
            {
                System.Console.WriteLine("Exact solution: Total return: " +
                			  TestInstance.p.getObjVal());
                for(s=0;s<NSHARES;s++) +=": " else solution is void XPRBbasis int double solval=0, fsol=new XPRSprob xprsp=p.getXPRSprob(); xprsp.CutStrategy=0; Disable automatic cuts xprsp.Presolve=0; Switch presolve off TOL=xprsp.FeasTol; Get feasibility tolerance Solve the LP-problem basis=p.saveBasis(); Save current Fix all variables for which at or a relatively large value for(s=0;s<NSHARES;s++) values of fsol[s]=frac[s].getSol();> 0,2-TOL)
                    buy[s].setLB(1);
            }

            p.mipOptimize();                     // Solve the MIP-problem


            // If an integer feas. solution was found...
            ifmipsol=0;
            if(p.getMIPStat()==4 || p.getMIPStat()==6)
            {
                ifmipsol=1;

                // ...get the value of the best solution
                solval=p.getObjVal();
                System.Console.WriteLine("Heuristic solution: Total return: " +
                			  p.getObjVal());
                for(s=0;s<NSHARES;s++) +=": " Re-initialize the MIP search Reset variables to their original bounds for(s=0;s<NSHARES;s++)> 0,2-TOL))
                {
                    buy[s].setLB(0);
                    buy[s].setUB(1);
                }

            /* Load the saved basis: bound changes are
            immediately passed on from BCL to the
            Optimizer if the problem has not been modified
            in any other way, so that there is no need to
            reload the matrix */
	        p.loadBasis(basis);

             // No need to store the saved basis any longer
            basis.reset();

            // Set the cutoff to the best known solution
            if (ifmipsol == 1)
                xprsp.MIPAbsCutoff = solval + TOL;

        }

    }

}