// --------------------------------------------------------------------------
// File: examples/src/LPex1.cs
// Version 9.0
// --------------------------------------------------------------------------
// Copyright (C) 2001-2003 by ILOG.
// All Rights Reserved.
// Permission is expressly granted to use this example in the
// course of developing applications that use ILOG products.
// --------------------------------------------------------------------------
//
// LPex1.cs - Entering and optimizing an LP problem
//
// Demonstrates different methods for creating a problem. The user has to
// choose the method on the command line:
//
// LPex1 -r generates the problem by adding constraints
// LPex1 -c generates the problem by adding variables
// LPex1 -n generates the problem by adding expressions
//
using ILOG.CONCERT;
using ILOG.CPLEX;
public class LPex1 {
internal static void Usage() {
System.Console.WriteLine("usage: LPex1 <option>");
System.Console.WriteLine("options: -r build model row by row");
System.Console.WriteLine("options: -c build model column by column");
System.Console.WriteLine("options: -n build model nonzero by nonzero");
}
public static void Main(string[] args) {
if ( args.Length != 1 || args[0].ToCharArray()[0] != `-' ) {
Usage();
return;
}
try {
// Create the modeler/solver object
Cplex cplex = new Cplex();
INumVar[][] var = new INumVar[1][];
IRange[][] rng = new IRange[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].ToCharArray()[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.Output().WriteLine("Solution status = " + cplex.GetStatus());
cplex.Output().WriteLine("Solution value = " + cplex.ObjValue);
int ncols = cplex.Ncols;
for (int j = 0; j < ncols; ++j) {
cplex.Output().WriteLine("Column: "
+ j +" Value = "
+ x[j]
+" Reduced cost = "
+ dj[j]);
}
int nrows = cplex.Nrows;
for (int i = 0; i < nrows; ++i) {
cplex.Output().WriteLine("Row : " + i
+" Slack = "
+ slack[i]
+" Pi = "
+ pi[i]);
}
}
cplex.End();
}
catch (ILOG.CONCERT.Exception e) {
System.Console.WriteLine("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 IMPModeler API
internal static void PopulateByRow(IMPModeler model,
INumVar[][] var,
IRange[][] rng) {
double[] lb = {0.0, 0.0, 0.0};
double[] ub = {40.0,
System.Double.MaxValue,
System.Double.MaxValue};
INumVar[] 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 IRange[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);
}
internal static void PopulateByColumn(IMPModeler model,
INumVar[][] var,
IRange[][] rng) {
IObjective obj = model.AddMaximize();
rng[0] = new IRange[2];
rng[0][0] = model.AddRange(-System.Double.MaxValue, 20.0);
rng[0][1] = model.AddRange(-System.Double.MaxValue, 30.0);
IRange r0 = rng[0][0];
IRange r1 = rng[0][1];
var[0] = new INumVar[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, System.Double.MaxValue);
var[0][2] = model.NumVar(model.Column(obj, 3.0).And(
model.Column(r0, 1.0).And(
model.Column(r1, 1.0))),
0.0, System.Double.MaxValue);
}
internal static void PopulateByNonzero(IMPModeler model,
INumVar[][] var,
IRange[][] rng) {
double[] lb = {0.0, 0.0, 0.0};
double[] ub = {40.0,
System.Double.MaxValue,
System.Double.MaxValue};
INumVar[] 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 IRange[2];
rng[0][0] = model.AddRange(-System.Double.MaxValue, 20.0);
rng[0][1] = model.AddRange(-System.Double.MaxValue, 30.0);
rng[0][0].Expr = model.Sum(model.Prod(-1.0, x[0]),
model.Prod( 1.0, x[1]),
model.Prod( 1.0, x[2]));
rng[0][1].Expr = model.Sum(model.Prod( 1.0, x[0]),
model.Prod(-3.0, x[1]),
model.Prod( 1.0, x[2]));
}
}
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