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1   /*
2    * Licensed to the Hipparchus project under one or more
3    * contributor license agreements.  See the NOTICE file distributed with
4    * this work for additional information regarding copyright ownership.
5    * The Hipparchus project licenses this file to You under the Apache License, Version 2.0
6    * (the "License"); you may not use this file except in compliance with
7    * the License.  You may obtain a copy of the License at
8    *
9    *      https://www.apache.org/licenses/LICENSE-2.0
10   *
11   * Unless required by applicable law or agreed to in writing, software
12   * distributed under the License is distributed on an "AS IS" BASIS,
13   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14   * See the License for the specific language governing permissions and
15   * limitations under the License.
16   */
17  package org.hipparchus.optim.nonlinear.vector.constrained;
18  
19  import org.hipparchus.optim.OptimizationData;
20  import org.hipparchus.optim.nonlinear.scalar.ObjectiveFunction;
21  import org.junit.Assert;
22  import org.junit.Test;
23  
24  public class SQPOptimizerGMTest extends AbstractTestAbstractSQPOptimizerTest {
25  
26      protected ConstraintOptimizer buildOptimizer() {
27          return new SQPOptimizerGM();
28      }
29  
30      @Test
31      public void testWithEqualityConstraintsOnly() {
32          final QuadraticFunction q = new QuadraticFunction(new double[][] { { 1.0, 0.0 }, { 0.0, 1.0 } },
33                  new double[] { 1.0, 0.0 },
34                  0.0);
35  
36          final LinearEqualityConstraint eqc = new LinearEqualityConstraint(new double[][] { { 1.0, 0.0 } },
37                  new double[] { 1.0 });
38  
39          final ConstraintOptimizer optimizer = buildOptimizer();
40          final OptimizationData[] data = new OptimizationData[] { new ObjectiveFunction(q), eqc };
41          final LagrangeSolution    solution  = optimizer.optimize(data);
42  
43          Assert.assertEquals(1.5, solution.getValue(), 1.e-4);
44      }
45  
46      @Test
47      public void testWithInequalityConstraintsOnly() {
48          final QuadraticFunction q = new QuadraticFunction(new double[][] { { 1.0, 0.0 }, { 0.0, 1.0 } },
49                  new double[] { 1.0, 0.0 },
50                  0.0);
51  
52          final LinearInequalityConstraint eqc = new LinearInequalityConstraint(new double[][] { { 1.0, 0.0 } },
53                  new double[] { 1.0 });
54  
55          final ConstraintOptimizer optimizer = buildOptimizer();
56          final OptimizationData[] data = new OptimizationData[] { new ObjectiveFunction(q), eqc };
57          final LagrangeSolution    solution  = optimizer.optimize(data);
58  
59          Assert.assertEquals(1.5, solution.getValue(), 1.e-4);
60      }
61  
62  }