5.1.4. Java 的LP建模与优化

在本节中,我们将使用 MindOpt JAVA API,以按行输入的形式来建模以及求解 线性规划问题示例 中的问题。

首先,引入 Java 包:

22import com.alibaba.damo.mindopt.*;

并创建优化模型:

28        MDOEnv env = new MDOEnv(); 
29        MDOModel model = new MDOModel(env); 
30        model.set(MDO.StringAttr.ModelName, "LP_01");

接下来,我们通过 MDOModel.set 设置模型属性 ModelSense,将目标函数设置为 最小化,并调用 MDOModel.addVar 来添加四个优化变量,定义其下界、上界、类型和名称(有关模型属性内容及其设置可参考 属性, 其他API请参考 JAVA API):

33            // Change to minimization problem.
34            model.set(MDO.IntAttr.ModelSense, MDO.MINIMIZE);
35
36            // Add variables.
37            MDOVar[] x = new MDOVar[4];
38            x[0] = model.addVar(0.0,         10.0, 1.0, 'C', "x0");
39            x[1] = model.addVar(0.0, MDO.INFINITY, 2.0, 'C', "x1");
40            x[2] = model.addVar(0.0, MDO.INFINITY, 1.0, 'C', "x2");
41            x[3] = model.addVar(0.0, MDO.INFINITY, 1.0, 'C', "x3");

接着,我们开始添加线性约束:

43            // Add constraints.
44            double[][] consV = new double[][] {
45                { 1.0, 1.0, 2.0, 3.0},
46                { 1.0, 0,  -1.0, 6.0} 
47            };
48
49            MDOLinExpr tempLinExpr1 = new MDOLinExpr();
50            tempLinExpr1.addTerms(consV[0], x);
51            model.addConstr(tempLinExpr1, MDO.GREATER_EQUAL, 1.0, "c0");
52
53            MDOLinExpr tempLinExpr2 = new MDOLinExpr();
54            tempLinExpr2.addTerms(consV[1], x);
55            model.addConstr(tempLinExpr2, MDO.EQUAL, 1.0, "c1");    

问题输入完成后,再调用 MDOModel.optimize 求解优化问题:

57            // Step 3. Solve the problem and populate optimization result.
58            model.optimize();

求解完成后,用 MDOModel.get 和模型属性值 ObjVal 来查看优化结果和最优目标值,以及 MDOVar.get 和变量属性值 X 来查看优化解的目标值。 其他的属性值请查看 属性 章节。

60            if (model.get(MDO.IntAttr.Status) == MDO.OPTIMAL) {
61                System.out.println("Optimal objective value is: " + model.get(MDO.DoubleAttr.ObjVal));
62                System.out.println("Decision variables: ");
63                for (int i = 0; i < 4; i++) {
64                    System.out.println( "x[" + i + "] = " + x[i].get(MDO.DoubleAttr.X));
65                }
66            }
67            else {
68                System.out.println("No feasible solution.");
69            }

示例 MdoLoEx1.java 提供了完整源代码:

 1/*
 2 *  Description
 3 *  -----------
 4 *
 5 *  Linear optimization (row-wise input).
 6 *
 7 *  Formulation
 8 *  -----------
 9 *
10 *  Minimize
11 *    obj: 1 x0 + 2 x1 + 1 x2 + 1 x3
12 *  Subject To
13 *   c1 : 1 x0 + 1 x1 + 2 x2 + 3 x3 >= 1
14 *   c2 : 1 x0 - 1 x2 + 6 x3 = 1
15 *  Bounds
16 *    0 <= x0 <= 10
17 *    0 <= x1
18 *    0 <= x2
19 *    0 <= x3
20 *  End
21 */
22import com.alibaba.damo.mindopt.*;
23import java.util.*;
24
25public class MdoLoEx1 { 
26    public static void main(String[] args) throws MDOException {
27        // Create model
28        MDOEnv env = new MDOEnv(); 
29        MDOModel model = new MDOModel(env); 
30        model.set(MDO.StringAttr.ModelName, "LP_01");
31
32        try {
33            // Change to minimization problem.
34            model.set(MDO.IntAttr.ModelSense, MDO.MINIMIZE);
35
36            // Add variables.
37            MDOVar[] x = new MDOVar[4];
38            x[0] = model.addVar(0.0,         10.0, 1.0, 'C', "x0");
39            x[1] = model.addVar(0.0, MDO.INFINITY, 2.0, 'C', "x1");
40            x[2] = model.addVar(0.0, MDO.INFINITY, 1.0, 'C', "x2");
41            x[3] = model.addVar(0.0, MDO.INFINITY, 1.0, 'C', "x3");
42
43            // Add constraints.
44            double[][] consV = new double[][] {
45                { 1.0, 1.0, 2.0, 3.0},
46                { 1.0, 0,  -1.0, 6.0} 
47            };
48
49            MDOLinExpr tempLinExpr1 = new MDOLinExpr();
50            tempLinExpr1.addTerms(consV[0], x);
51            model.addConstr(tempLinExpr1, MDO.GREATER_EQUAL, 1.0, "c0");
52
53            MDOLinExpr tempLinExpr2 = new MDOLinExpr();
54            tempLinExpr2.addTerms(consV[1], x);
55            model.addConstr(tempLinExpr2, MDO.EQUAL, 1.0, "c1");    
56    
57            // Step 3. Solve the problem and populate optimization result.
58            model.optimize();
59
60            if (model.get(MDO.IntAttr.Status) == MDO.OPTIMAL) {
61                System.out.println("Optimal objective value is: " + model.get(MDO.DoubleAttr.ObjVal));
62                System.out.println("Decision variables: ");
63                for (int i = 0; i < 4; i++) {
64                    System.out.println( "x[" + i + "] = " + x[i].get(MDO.DoubleAttr.X));
65                }
66            }
67            else {
68                System.out.println("No feasible solution.");
69            }
70        } catch (Exception e) { 
71            System.out.println("Exception during optimization");
72            e.printStackTrace();
73        } finally { 
74            model.dispose();
75            env.dispose();
76        }
77    }
78}