// --------------------------------------------------------------------------
// File: examples/src/LPex1.java
// Version 8.1
// --------------------------------------------------------------------------
// Copyright (C) 2001-2002 by ILOG.
// All Rights Reserved.
// Permission is expressly granted to use this example in the
// course of developing applications that use ILOG products.
// --------------------------------------------------------------------------
//
// LPex1.java - Entering and optimizing an LP problem
//
// Demonstrates different methods for creating a problem. The user has to
// choose the method on the command line:
//
// java LPex1 -r generates the problem by adding constraints
// java LPex1 -c generates the problem by adding variables
// java LPex1 -n generates the problem by adding expressions
//
import ilog.concert.*;
import ilog.cplex.*;
public class LPex1 {
static void usage() {
System.out.println("usage: LPex1 <option>");
System.out.println("options: -r build model row by row");
System.out.println("options: -c build model column by column");
System.out.println("options: -n build model nonzero by nonzero");
}
public static void main(String[] args) {
if ( args.length != 1 || args[0].charAt(0) != '-' ) {
usage();
return;
}
try {
// Create the modeler/solver object
IloCplex cplex = new IloCplex();
IloNumVar[][] var = new IloNumVar[1][];
IloRange[][] rng = new IloRange[1][];
// Evaluate command line option and call appropriate populate method.
// The created ranges and variables are returned as element 0 of arrays
// var and rng.
switch ( args[0].charAt(1) ) {
case 'r': populateByRow(cplex, var, rng);
break;
case 'c': populateByColumn(cplex, var, rng);
break;
case 'n': populateByNonzero(cplex, var, rng);
break;
default: usage();
return;
}
// write model to file
cplex.exportModel("lpex1.lp");
// solve the model and display the solution if one was found
if ( cplex.solve() ) {
double[] x = cplex.getValues(var[0]);
double[] dj = cplex.getReducedCosts(var[0]);
double[] pi = cplex.getDuals(rng[0]);
double[] slack = cplex.getSlacks(rng[0]);
cplex.out().println("Solution status = " + cplex.getStatus());
cplex.out().println("Solution value = " + cplex.getObjValue());
int ncols = cplex.getNcols();
for (int j = 0; j < ncols; ++j) {
cplex.out().println("Column: " + j +
" Value = " + x[j] +
" Reduced cost = " + dj[j]);
}
int nrows = cplex.getNrows();
for (int i = 0; i < nrows; ++i) {
cplex.out().println("Row : " + i +
" Slack = " + slack[i] +
" Pi = " + pi[i]);
}
}
cplex.end();
}
catch (IloException e) {
System.err.println("Concert exception '" + e + "' caught");
}
}
// The following methods all populate the problem with data for the following
// linear program:
//
// Maximize
// x1 + 2 x2 + 3 x3
// Subject To
// - x1 + x2 + x3 <= 20
// x1 - 3 x2 + x3 <= 30
// Bounds
// 0 <= x1 <= 40
// End
//
// using the IloMPModeler API
static void populateByRow(IloMPModeler model,
IloNumVar[][] var,
IloRange[][] rng) throws IloException {
double[] lb = {0.0, 0.0, 0.0};
double[] ub = {40.0, Double.MAX_VALUE, Double.MAX_VALUE};
IloNumVar[] x = model.numVarArray(3, lb, ub);
var[0] = x;
double[] objvals = {1.0, 2.0, 3.0};
model.addMaximize(model.scalProd(x, objvals));
rng[0] = new IloRange[2];
rng[0][0] = model.addLe(model.sum(model.prod(-1.0, x[0]),
model.prod( 1.0, x[1]),
model.prod( 1.0, x[2])), 20.0);
rng[0][1] = model.addLe(model.sum(model.prod( 1.0, x[0]),
model.prod(-3.0, x[1]),
model.prod( 1.0, x[2])), 30.0);
}
static void populateByColumn(IloMPModeler model,
IloNumVar[][] var,
IloRange[][] rng) throws IloException {
IloObjective obj = model.addMaximize();
rng[0] = new IloRange[2];
rng[0][0] = model.addRange(-Double.MAX_VALUE, 20.0);
rng[0][1] = model.addRange(-Double.MAX_VALUE, 30.0);
IloRange r0 = rng[0][0];
IloRange r1 = rng[0][1];
var[0] = new IloNumVar[3];
var[0][0] = model.numVar(model.column(obj, 1.0).and(
model.column(r0, -1.0).and(
model.column(r1, 1.0))),
0.0, 40.0);
var[0][1] = model.numVar(model.column(obj, 2.0).and(
model.column(r0, 1.0).and(
model.column(r1, -3.0))),
0.0, Double.MAX_VALUE);
var[0][2] = model.numVar(model.column(obj, 3.0).and(
model.column(r0, 1.0).and(
model.column(r1, 1.0))),
0.0, Double.MAX_VALUE);
}
static void populateByNonzero(IloMPModeler model,
IloNumVar[][] var,
IloRange[][] rng) throws IloException {
double[] lb = {0.0, 0.0, 0.0};
double[] ub = {40.0, Double.MAX_VALUE, Double.MAX_VALUE};
IloNumVar[] x = model.numVarArray(3, lb, ub);
var[0] = x;
double[] objvals = {1.0, 2.0, 3.0};
model.add(model.maximize(model.scalProd(x, objvals)));
rng[0] = new IloRange[2];
rng[0][0] = model.addRange(-Double.MAX_VALUE, 20.0);
rng[0][1] = model.addRange(-Double.MAX_VALUE, 30.0);
rng[0][0].setExpr(model.sum(model.prod(-1.0, x[0]),
model.prod( 1.0, x[1]),
model.prod( 1.0, x[2])));
rng[0][1].setExpr(model.sum(model.prod( 1.0, x[0]),
model.prod(-3.0, x[1]),
model.prod( 1.0, x[2])));
}
}
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