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    * Licensed to the Hipparchus project under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The Hipparchus project licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
    *
    *      https://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
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  package org.hipparchus.optim.nonlinear.vector.constrained;
  
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.optim.InitialGuess;
  import org.hipparchus.optim.nonlinear.scalar.ObjectiveFunction;
  import org.junit.Assert;
  import org.junit.Test;
  
  public class ADMMQPOptimizerTest extends AbstractConstrainedOptimizerTest {
  
      protected ConstraintOptimizer buildOptimizer() {
          return new ADMMQPOptimizer();
      }
  
      @Test
      public void test1() {
          QuadraticFunction q = new QuadraticFunction(new double[][] { { 4.0, -2.0 }, { -2.0, 4.0 } },
                                                      new double[] { 6.0, 0.0 },
                                                      0.0);
  
          // y = 1
          LinearEqualityConstraint eqc = new LinearEqualityConstraint(new double[][] { { 0.0, 1.0 } },
                                                                      new double[] { 1.0 });
  
  
          // x > 0, y > 0, x + y > 2
          LinearInequalityConstraint ineqc = new LinearInequalityConstraint(new double[][] { { 1.0, 0.0 }, { 0.0, 1.0 }, { 1.0, 1.0 } },
                                                                            new double[] { 0.0, 0.0, 2.0 });
  
  
          doTestProblem(new double[] {  1, 1 },       2.5e-5,
                        new double[] { -6, 0, 0, 8 }, 2.6e-4,
                        8, 2.0e-4,
                        new ObjectiveFunction(q),
                        null,
                        eqc, ineqc);
  
      }
  
      @Test
      public void test2() {
          QuadraticFunction q = new QuadraticFunction(new double[][] { { 6.0, 2.0 }, { 2.0, 8.0 } },
                                                      new double[] { 5.0, 1.0 },
                                                      0.0);
  
          // constraint x + 2y = 1
          LinearEqualityConstraint eqc = new LinearEqualityConstraint(new double[][] { { 1.0, 2.0 } },
                                                                      new double[] { 1.0 });
  
          // x > 0, y > 0
          LinearInequalityConstraint ineqc = new LinearInequalityConstraint(new double[][] { { 1.0, 0.0 }, { 0.0, 1.0 } },
                                                                            new double[] { 0.0, 0.0 });
  
  
          doTestProblem(new double[] {  0, 0.5 },     1.1e-5,
                        new double[] { 2.5, 3.5, 0 }, 8.5e-6,
                        1.5, 6.3e-5,
                        new ObjectiveFunction(q),
                        null,
                        eqc, ineqc);
  
      }
  
      @Test
      public void testBounded() {
          QuadraticFunction q = new QuadraticFunction(new double[][] { { 1.0, 0.0 }, { 0, 1.0 } },
                                                      new double[] { -3.0, -1.0 },
                                                      5.0);
  
          // 0 < x < 2, 0 < y < 2
          LinearBoundedConstraint ineqc = new LinearBoundedConstraint(new double[][] { { 1.0, 0.0 },{ 0.0, 1.0 }},
                                                                       new double[] { 0.0,0.0 }, new double[] { 2.0,2.0 });
  
  
          doTestProblem(new double[] { 2, 1 }, 4.6e-6,
                        new double[] { 1, 0 }, 3.1e-5,
                        0.5, 4.6e-6,
                        new ObjectiveFunction(q),
                        null,
                        ineqc);
  
      }
  
     @Test
     public void testOptimizeWithGuess() {
         // GIVEN
         final ADMMQPOptimizer optimizer = createOptimizerOnSimpleProblem(false, false);
         optimizer.parseOptimizationData(new InitialGuess(new double[2]));
 
         // WHEN
         final LagrangeSolution solution  = optimizer.optimize();
 
         // THEN
         Assert.assertNotNull(optimizer.getConvergenceChecker());
         Assert.assertTrue(optimizer.isConverged());
         Assert.assertEquals(0., solution.getValue(), 0);
     }
 
     @Test
     public void testOptimizeWithoutScaling() {
         // GIVEN
         final ADMMQPOptimizer optimizer = createOptimizerOnSimpleProblem(false, false);
 
         // WHEN
         final LagrangeSolution solution  = optimizer.optimize();
 
         // THEN
         Assert.assertNotNull(optimizer.getConvergenceChecker());
         Assert.assertTrue(optimizer.isConverged());
         Assert.assertEquals(0., solution.getValue(), 0);
     }
 
     @Test
     public void testOptimizeWithPolishWithScaling() {
         // GIVEN
         final ADMMQPOptimizer optimizer = createOptimizerOnSimpleProblem(true, true);
 
         // WHEN
         final LagrangeSolution solution  = optimizer.optimize();
 
         // THEN
         Assert.assertNotNull(optimizer.getConvergenceChecker());
         Assert.assertTrue(optimizer.isConverged());
         Assert.assertEquals(0., solution.getValue(), 0);
     }
 
     @Test
     public void testOptimizeWithPolishWithoutScaling() {
         // GIVEN
         final ADMMQPOptimizer optimizer = createOptimizerOnSimpleProblem(true, false);
 
         // WHEN
         final LagrangeSolution solution  = optimizer.optimize();
 
         // THEN
         Assert.assertNotNull(optimizer.getConvergenceChecker());
         Assert.assertTrue(optimizer.isConverged());
         Assert.assertEquals(0., solution.getValue(), 0);
     }
 
     private ADMMQPOptimizer createOptimizerOnSimpleProblem(final boolean polishing, final boolean scaling) {
         final QuadraticFunction q = new QuadraticFunction(new double[][] { { 1.0, 0.0 }, { 0.0, 1.0 } },
                 new double[] { 0.0, 0.0 }, 0.0);
         final LinearEqualityConstraint eqc = new LinearEqualityConstraint(new double[][] { { 1.0, 1.0 } },
                 new double[] { 0.0 });
 
         final ADMMQPOption option = new ADMMQPOption();
         option.setPolishing(polishing);
         option.setScaling(scaling);
 
         final ADMMQPOptimizer optimizer = new ADMMQPOptimizer();
         optimizer.parseOptimizationData(new ObjectiveFunction(q), eqc, option);
         return optimizer;
     }
 
     @Test
     public void testParseOptimizationDataException() {
         // GIVEN
         final ADMMQPOptimizer testSQPOptimizer = new ADMMQPOptimizer();
         final EqualityConstraint equalityConstraint = new LinearEqualityConstraint(new double[1][1], new double[1]);
         final QuadraticFunction multivariateFunction = new QuadraticFunction(new double[1][1], new double[1], 0);
         final ObjectiveFunction objectiveFunction = new ObjectiveFunction(multivariateFunction);
         // WHEN
         try {
             testSQPOptimizer.parseOptimizationData(objectiveFunction, equalityConstraint);
             Assert.fail();
         } catch (final MathIllegalArgumentException exception) {
             Assert.assertEquals("rank of constraints must be lesser than domain dimension, but 1 >= 1",
                     exception.getMessage());
         }
     }
 
 }