Example 2: Linear Constrained Optimization

subject to

is solved with an initial guess of , and .

using System;
using Imsl.Math;

public class MinConGenLinEx2 : MinConGenLin.IGradient
{
	public double F(double[] x)
	{
		return - x[0] * x[1] * x[2];
	}
	
	public void Gradient(double[] x, double[] g)
	{
		g[0] = - x[1] * x[2];
		g[1] = - x[0] * x[2];
		g[2] = - x[0] * x[1];
	}
	
	public static void  Main(String[] args)
	{
		int neq = 0;
		int ncon = 2;
		int nvar = 3;
		double[] a = new double[]{- 1.0, - 2.0, - 2.0, 1.0, 2.0, 2.0};
		double[] xlb = new double[]{0.0, 0.0, 0.0};
		double[] xub = new double[]{20.0, 11.0, 42.0};
		double[] b = new double[]{0.0, 72.0};
		
		MinConGenLin.IGradient fcn = new MinConGenLinEx2();
		MinConGenLin zf = new MinConGenLin(fcn, nvar, ncon, neq, a, b,
			xlb, xub);
		zf.SetGuess(new double[]{10.0, 10.0, 10.0});
		zf.Solve();
		new PrintMatrix("Solution").Print(zf.GetSolution());
		Console.Out.WriteLine("Objective value = " + 
			zf.ObjectiveValue);
	}
}

Output

Solution
   0   
0  20  
1  11  
2  15  

Objective value = -3300