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Folio - Examples from 'Getting Started'

Different versions of a portfolio optimization problem.

Basic modelling and solving tasks:
  • modeling and solving a small LP problem (foliolp)
  • performing explicit initialization (folioini*)
  • data input from file, index sets (foliodata, requires foliocpplp.dat)
  • modeling and solving a small MIP problem with binary variables (foliomip1)
  • modeling and solving a small MIP problem with semi-continuous variables (foliomip2)
  • modeling and solving QP and MIQP problems (folioqp, requires foliocppqp.dat)
  • modeling and solving QCQP problems (folioqc, requires foliocppqp.dat)
  • heuristic solution of a MIP problem (folioheur)
Advanced modeling and solving tasks:
  • enlarged version of the basic MIP model (foliomip3 with include file readfoliodata.c_, to be used with data set folio10.cdat)
  • defining an integer solution callback (foliocb)
  • using the MIP solution pool (foliosolpool)
  • using the solution enumerator (folioenumsol)
  • handling infeasibility through deviation variables (folioinfeas)
  • retrieving IIS (folioiis)
  • using the built-in infeasibility repair functionality (foliorep)
Further explanation of this example: 'Getting Started with BCL' for the basic modelling and solving tasks; 'Advanced Evaluators Guide' for solution enumeration and infeasibilit handling[download all files]

Source Files

Data Files


  Xpress-BCL C++ Example Problems

  file foliomip1.cpp
  Modeling a small MIP problem
  to perform portfolio optimization.
   -- Limiting the total number of assets --

  (c) 2008 Fair Isaac Corporation
      author: S.Heipcke, Aug. 2003, rev. Mar. 2011

#include <iostream>
#include "xprb_cpp.h"

using namespace std;
using namespace ::dashoptimization;

#define MAXNUM 4                   // Max. number of different assets

#define NSHARES 10                 // Number of shares
#define NRISK 5                    // Number of high-risk shares
#define NNA 4                      // Number of North-American shares

double RET[] = {5,17,26,12,8,9,7,6,31,21};  // Estimated return in investment
int RISK[] = {1,2,3,8,9};          // High-risk values among shares
int NA[] = {0,1,2,3};              // Shares issued in N.-America

int main(int argc, char **argv)
 int s;
 XPRBprob p("FolioMIP1");          // Initialize a new problem in BCL
 XPRBexpr Risk,Na,Return,Cap,Num;
 XPRBvar frac[NSHARES];            // Fraction of capital used per share
 XPRBvar buy[NSHARES];             // 1 if asset is in portfolio, 0 otherwise

// Create the decision variables (including upper bounds for `frac')
  frac[s] = p.newVar("frac", XPRB_PL, 0, 0.3);
  buy[s] = p.newVar("buy", XPRB_BV);

// Objective: total return
 for(s=0;s<NSHARES;s++) Return += RET[s]*frac[s];
 p.setObj(Return);                // Set the objective function

// Limit the percentage of high-risk values
 for(s=0;s<NRISK;s++) Risk += frac[RISK[s]];
 p.newCtr(Risk <= 1.0/3);

// Minimum amount of North-American values
 for(s=0;s<NNA;s++) Na += frac[NA[s]];
 p.newCtr(Na >= 0.5);

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

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

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

// Solve the problem

 char *MIPSTATUS[] = {"not loaded", "not optimized", "LP optimized",
                      "unfinished (no solution)",
                      "unfinished (solution found)", "infeasible", "optimal",

 cout << "Problem status: " << MIPSTATUS[p.getMIPStat()] << endl;

// Solution printing
 cout << "Total return: " << p.getObjVal() << endl;
  cout << s << ": " << frac[s].getSol()*100 << "% (" << buy[s].getSol()
       << ")" << endl;

 return 0;

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