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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 folioqp.cpp
  Modeling a small QCQP problem
  to perform portfolio optimization.
  -- Maximize return with limit on variance ---

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

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

using namespace std;
using namespace ::dashoptimization;

#define DATAFILE XPRBDATAPATH "/GS/foliocppqp.dat"

#define MAXVAR 0.55                // Max. allowed variance

#define NSHARES 10                 // Number of 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 NA[] = {0,1,2,3};              // Shares issued in N.-America
double VAR[NSHARES][NSHARES];      // Variance/covariance matrix of
                                   // estimated returns

int main(int argc, char **argv)
 int s,t;
 XPRBprob p("FolioQC");            // Initialize a new problem in BCL
 XPRBexpr Na,Return,Cap,Variance;
 XPRBvar frac[NSHARES];            // Fraction of capital used per share
 FILE *datafile;

// Read `VAR' data from file
  XPRBreadarrlinecb(XPRB_FGETS, datafile, 200, "g ", VAR[s], NSHARES);

// Create the decision variables
  frac[s] = p.newVar(XPRBnewname("frac(%d)",s+1), XPRB_PL, 0, 0.3);

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

// 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 variance
  for(t=0;t<NSHARES;t++) Variance += VAR[s][t]*frac[s]*frac[t];
 p.newCtr(Variance <= MAXVAR);

// Solve the problem

// Solution printing
 cout << "With a max. variance of " << MAXVAR << " total return is " <<
         p.getObjVal() << endl;
  cout << s << ": " << frac[s].getSol()*100 << "%" << endl;

 return 0;

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