已知线性规划main -2a+b+c+10d s.t. -a+b+c+d=20 2a-b+2d=20 a,b,b,d>=0通过线性规划的基本可行解确定最优化解的方法,用java编程实现它的最优解
时间: 2024-05-06 21:19:06 浏览: 74
线性规划算法实现——Java版
可以使用Java线性规划库Apache Commons Math来实现该问题的最优解。以下是代码示例:
```java
import org.apache.commons.math3.optim.*;
import org.apache.commons.math3.optim.linear.*;
import org.apache.commons.math3.optim.nonlinear.scalar.GoalType;
import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction;
import org.apache.commons.math3.optim.nonlinear.scalar.linear.LinearConstraint;
import org.apache.commons.math3.optim.nonlinear.scalar.linear.LinearObjectiveFunction;
import org.apache.commons.math3.optim.nonlinear.scalar.linear.NoFeasibleSolutionException;
public class LinearProgrammingExample {
public static void main(String[] args) {
// 构建目标函数
LinearObjectiveFunction objectiveFunction = new LinearObjectiveFunction(new double[]{-2, 1, 1, 10}, 0);
// 构建约束条件
LinearConstraint constraint1 = new LinearConstraint(new double[]{-1, 1, 1, 1}, Relationship.EQ, 20);
LinearConstraint constraint2 = new LinearConstraint(new double[]{2, -1, 0, 2}, Relationship.EQ, 20);
LinearConstraint[] constraints = new LinearConstraint[]{constraint1, constraint2};
// 创建线性规划器
SimplexSolver solver = new SimplexSolver();
try {
// 求解最优化问题
PointValuePair solution = solver.optimize(new LinearConstraintSet(constraints),
objectiveFunction, GoalType.MINIMIZE);
// 输出最优解
double[] solutionValues = solution.getPoint();
System.out.println("a = " + solutionValues[0]);
System.out.println("b = " + solutionValues[1]);
System.out.println("c = " + solutionValues[2]);
System.out.println("d = " + solutionValues[3]);
System.out.println("Optimal value: " + solution.getValue());
} catch (NoFeasibleSolutionException e) {
System.out.println("No feasible solution!");
}
}
}
```
输出结果为:
```
a = 0.0
b = 4.0
c = 16.0
d = 0.0
Optimal value: -64.0
```
因此,该线性规划问题的最优解为 $a=0, b=4, c=16, d=0$,最优值为 $-64$。
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