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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 foliodata.cpp
  Modeling a small LP problem
  to perform portfolio optimization.
  -- Data input from file --

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

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

using namespace std;
using namespace ::dashoptimization;

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

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

double RET[NSHARES];                 // Estimated return in investment
char RISK[][100] = {"hardware", "theater", "telecom", "software",
                    "electronics"};  // High-risk values among shares
char NA[][100] = {"treasury", "hardware", "theater", "telecom"};
                                     // Shares issued in N.-America

XPRBindexSet SHARES;                 // Set of shares

void readData(XPRBprob &p)
 double value;
 int s;
 FILE *datafile;
 char name[100];

 SHARES=p.newIndexSet("Shares",NSHARES);  // Create the `SHARES' index set

// Read `RET' data from file
  XPRBreadlinecb(XPRB_FGETS, datafile, 200, "T g", name, &value);

 SHARES.print();                     // Print out the set contents

int main(int argc, char **argv)
 int s;
 XPRBexpr Risk,Na,Return,Cap;
 XPRBvar frac[NSHARES];              // Fraction of capital used per share

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

// Read data from file

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

// 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[SHARES[RISK[s]]];
 p.newCtr("Risk", Risk <= 1.0/3);

// Minimum amount of North-American values
 for(s=0;s<NNA;s++) Na += frac[SHARES[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);

// Solve the problem

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

 return 0;

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