Java使用google-ortools库的方法

叁歲伎倆 2021-12-23 16:57 363阅读 0赞

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1）加载动态链接

这个是windows环境，就是后缀为.dll的文件；

2）把下载的文件拷贝到C:\\Windows\\System32目录下

3）加载一下maven依赖，如:

<dependency>
          <groupId>io.github.oliviercailloux</groupId>
          <artifactId>google-or-tools</artifactId>
          <version>6.7.2</version>
     </dependency>

--------------------

准备工作做好了，现在就可以跑Java程序了，如下例子：

import com.google.ortools.linearsolver.MPConstraint;
    import com.google.ortools.linearsolver.MPObjective;
    import com.google.ortools.linearsolver.MPSolver;
    import com.google.ortools.linearsolver.MPVariable;
    
    /** Simple linear programming example.*/
    public class LinearProgrammingExample {
      static {
        System.loadLibrary("jniortools");
      }
    
      public static void main(String[] args) throws Exception {
        MPSolver solver = new MPSolver(
            "LinearProgrammingExample", MPSolver.OptimizationProblemType.GLOP_LINEAR_PROGRAMMING);
    
        double infinity = java.lang.Double.POSITIVE_INFINITY;
        // x and y are continuous non-negative variables.
        MPVariable x = solver.makeNumVar(0.0, infinity, "x");
        MPVariable y = solver.makeNumVar(0.0, infinity, "y");
        System.out.println("Number of variables = " + solver.numVariables());
    
        // x + 2*y <= 14.
        MPConstraint c0 = solver.makeConstraint(-infinity, 14.0, "c0");
        c0.setCoefficient(x, 1);
        c0.setCoefficient(y, 2);
    
        // 3*x - y >= 0.
        MPConstraint c1 = solver.makeConstraint(0.0, infinity, "c1");
        c1.setCoefficient(x, 3);
        c1.setCoefficient(y, -1);
    
        // x - y <= 2.
        MPConstraint c2 = solver.makeConstraint(-infinity, 2.0, "c2");
        c2.setCoefficient(x, 1);
        c2.setCoefficient(y, -1);
        System.out.println("Number of constraints = " + solver.numConstraints());
    
        // Maximize 3 * x + 4 * y.
        MPObjective objective = solver.objective();
        objective.setCoefficient(x, 3);
        objective.setCoefficient(y, 4);
        objective.setMaximization();
    
        final MPSolver.ResultStatus resultStatus = solver.solve();
        // Check that the problem has an optimal solution.
        if (resultStatus != MPSolver.ResultStatus.OPTIMAL) {
          System.err.println("The problem does not have an optimal solution!");
          return;
        }
    
        // The value of each variable in the solution.
        System.out.println("Solution");
        System.out.println("x = " + x.solutionValue());
        System.out.println("y = " + y.solutionValue());
    
        // The objective value of the solution.
        System.out.println("Optimal objective value = " + solver.objective().value());
      }
    }

线性规划图如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3p5MzQ1MjkzNzIx_size_16_color_FFFFFF_t_70][]

最后得到的值：

Number of variables = 2
    Number of constraints = 3
    Solution:
    x = 6.0
    y = 4.0
    Optimal objective value = 34.0

[github]: https://github.com/zhangyu345293721
[LeetCode_55. Jump Game]: https://blog.csdn.net/zy345293721/article/details/84991870
[Leetcode_300. Longest Increasing Subsequence]: https://blog.csdn.net/zy345293721/article/details/84984249
[LeetCode_560. Subarray Sum Equals K_k]: https://blog.csdn.net/zy345293721/article/details/84952201
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3p5MzQ1MjkzNzIx_size_16_color_FFFFFF_t_70]: /images/20211223/b2103db168bd4731b91befd8c37b6ded.png