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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 foliolp.cpp
  Modeling a small LP problem
  to perform portfolio optimization.

  (c) 2008-2023 Fair Isaac Corporation
      author: S.Heipcke, Aug. 2003, rev. Mar. 2011

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

using namespace std;
using namespace ::dashoptimization;

#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("FolioLP");              // Initialize a new problem in BCL
 XPRBexpr Risk,Na,Return,Cap;
 XPRBvar frac[NSHARES];              // Fraction of capital used per share

// Create the decision variables
 for(s=0;s<NSHARES;s++) frac[s] = p.newVar("frac");  //, 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

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

/* Equivalent:
 XPRBctr CRisk;
 CRisk = p.newCtr("Risk");
 for(s=0;s<NRISK;s++) CRisk.addTerm(frac[RISK[s]], 1);

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

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

// Upper bounds on the investment per share
 for(s=0;s<NSHARES;s++) frac[s].setUB(0.3);

// Export matrix to a file
/* p.exportProb(XPRB_MPS, "Folio");
 p.exportProb(XPRB_LP, "Folio");

// Disable all BCL and Optimizer message printing, except error messages
// p.setMsgLevel(1);

// Solve the problem

 char *LPSTATUS[] = {"not loaded", "optimal", "infeasible",
          "worse than cutoff", "unfinished", "unbounded", "cutoff in dual",
	  "unsolved", "nonconvex"};

 cout << "Problem status: " << LPSTATUS[p.getLPStat()] << endl;

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

 return 0;

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