用java按以下要求编写代码，要有过程显示，要有注释：用单纯形法解线性规划：min z=-2*x1-3*x2 s.t. -x1+x2<=2; x1+2*x2<=10; 3*x1+x2<=15; x1,x2>=0.

在Java中，我们可以使用Apache Commons Math库来实现单纯形法解线性规划问题。首先，需要添加Apache Commons Math依赖。以下是详细的步骤和代码示例：

**1. 添加依赖**
如果你使用Maven，可以在pom.xml文件中添加如下依赖：

<dependencies>
    <dependency>
        <groupId>org.apache.commons</groupId>
        <artifactId>commons-math3</artifactId>
        <version>3.6.1</version>
    </dependency>
</dependencies>

**2. 定义线性规划模型**

import org.apache.commons.math3.optim.*;
import org.apache.commons.math3.optim.linear.*;

// 创建线性目标函数
LinearObjectiveFunction objective = new LinearObjectiveFunction(new double[]{-2, -3}, 0); // min z = -2x1 - 3x2

// 定义线性约束
LinearConstraint constraint1 = new LinearConstraint(new double[]{-1, 1}, Relationship.LEQ, 2);
LinearConstraint constraint2 = new LinearConstraint(new double[]{1, 2}, Relationship.LEQ, 10);
LinearConstraint constraint3 = new LinearConstraint(new double[]{3, 1}, Relationship.LEQ, 15);

// 构建约束列表
List<LinearConstraint> constraints = Arrays.asList(constraint1, constraint2, constraint3);

// 线性变量范围
PointValuePair[] bounds = new PointValuePair[2];
bounds[0] = new PointValuePair(0, 0); // x1 >= 0
bounds[1] = new PointValuePair(Double.MAX_VALUE, Double.MAX_VALUE); // x2 >= 0 (这里设为无限大，实际项目中应适当设定)

// 创建线性优化问题
OptimizationProblem problem = new LinearConstraintOptimizationProblem(objective, constraints, bounds);

// 解决问题
SimplexSolver solver = new SimplexSolver();
PointValuePair solution = solver.optimize(problem);

// 输出结果
System.out.println("最优解:");
System.out.printf("x1 = %.2f, x2 = %.2f\n", solution.getPoint()[0], solution.getPoint()[1]);
System.out.println("最小值: " + solution.getValue());

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