cplex-Java-样例代码解析

cplex-Java-样例代码解析

import ilog.cplex.IloCplex;
import ilog.concert.*;

/**
 * 
 * 
 * 
 * 最大化 x1 + 2x2 + 3x3

 * 约束 

 * –x1 + x2 . + x3 ≤ 20

 * x1 – 3x2 + x3 ≤ 30

 * 使用这些界限 

 * 0 ≤ x1 ≤ 40

 * 0 ≤ x2 ≤ infinity

 * 0 ≤ x3 ≤ infinity

 * 
 * 
 * 
 * @author caicai
 *
 */
public class Color
{
	static
	{
		System.loadLibrary("cplex1271");
	}

	public static void main(String[] args) throws IloException
	{

		
			// 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.

			 populateByRow(cplex, var, rng);
			// populateByColumn(cplex, var, rng);
			//populateByNonzero(cplex, var, rng);

			// write model to file,保存模型
			cplex.exportModel("lpex1.lp");

			// solve the model and display the solution if one was found
			if (cplex.solve())
			{
				//var[0] = x,计算后各变量的取值
				double[] x = cplex.getValues(var[0]);
				double[] dj = cplex.getReducedCosts(var[0]);
				//对偶
				double[] pi = cplex.getDuals(rng[0]);
				double[] slack = cplex.getSlacks(rng[0]);
				//cplex.setWarning(System.err);
				cplex.output().println("totalTime = "+cplex.getCplexTime());
				
				cplex.output()
						.println("Solution status = " + cplex.getStatus());
				//f(x)的结果
				cplex.output()
						.println("Solution value  = " + cplex.getObjValue());

				int nvars = x.length;
				for (int j = 0; j < nvars; ++j)
				{
					cplex.output().println("Variable " + j + ": Value = " + x[j]
							+ " Reduced cost = " + dj[j]);
				}

				int ncons = slack.length;
				for (int i = 0; i < ncons; ++i)
				{
					cplex.output().println("Constraint " + i + ": Slack = "
							+ slack[i] + " Pi = " + pi[i]);
				}
			}
			cplex.end();
	}

	// 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
	{
		//变量取值下界
		double[] lb = { 0.0, 0.0, 0.0 };
		//变量取值上界
		double[] ub = { 40.0, Double.MAX_VALUE, Double.MAX_VALUE };
		//变量名
		String[] varname = { "x1", "x2", "x3" };
		//初始化变量,3是变量个数
		IloNumVar[] x = model.numVarArray(3, lb, ub, varname);
		var[0] = x;

		//变量前系数
		//y = x1 + 2x2 + 3x3
		//计算y的最大值
		double[] objvals = { 1.0, 2.0, 3.0 };
		model.addMaximize(model.scalProd(x, objvals));

		//约束条件
		rng[0] = new IloRange[2];
		//c1
		//–x1 + x2 . + x3 ≤ 20
		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, "c1");
		//c2
		//x1 – 3x2 + x3 ≤ 30
		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, "c2");
	}

	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, "c1");
		rng[0][1] = model.addRange(-Double.MAX_VALUE, 30.0, "c2");

		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, "x1");
		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, "x2");
		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, "x3");
	}

	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])));

		x[0].setName("x1");
		x[1].setName("x2");
		x[2].setName("x3");
		rng[0][0].setName("c1");
		rng[0][1].setName("c2");
	}
}

　　

posted on 2017-07-24 15:04 好吧,就是菜菜 阅读( ...) 评论( ...) 编辑 收藏

转载于:https://www.cnblogs.com/shuiyonglewodezzzzz/p/7229007.html