/*
    * 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.
   */
  package org.hipparchus.optim.nonlinear.vector.constrained;
  
  import org.hipparchus.exception.LocalizedCoreFormats;
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.linear.MatrixUtils;
  import org.hipparchus.linear.RealMatrix;
  import org.hipparchus.linear.RealVector;
  import org.hipparchus.optim.nonlinear.scalar.ObjectiveFunction;
  import org.junit.Assert;
  import org.junit.Test;
  
  public class AbstractSQPOptimizerTest {
  
      @Test
      public void testLagrangianGradX() {
          // GIVEN
          final TestSQPOptimizer testSQPOptimizer = new TestSQPOptimizer();
          final TwiceDifferentiableFunction multivariateFunction = new RosenbrockFunction();
          final ObjectiveFunction objectiveFunction = new ObjectiveFunction(multivariateFunction);
          testSQPOptimizer.parseOptimizationData(objectiveFunction);
          final RealVector expectedVector = MatrixUtils.createRealVector(new double[] { 1 });
          // WHEN
          final RealVector actualVector = testSQPOptimizer.lagrangianGradX(expectedVector, null,null, null);
          // THEN
          for (int i = 0; i < expectedVector.getDimension(); i++) {
              Assert.assertEquals(expectedVector.getEntry(i), actualVector.getEntry(i), 0);
          }
      }
  
      @Test
      public void testParseOptimizationData() {
          // GIVEN
          final TestSQPOptimizer testSQPOptimizer = new TestSQPOptimizer();
          final SQPOption expectedOptions = new SQPOption();
          final TwiceDifferentiableFunction multivariateFunction = new RosenbrockFunction();
          final ObjectiveFunction objectiveFunction = new ObjectiveFunction(multivariateFunction);
          // WHEN
          testSQPOptimizer.parseOptimizationData(objectiveFunction, expectedOptions);
          // THEN
          Assert.assertEquals(expectedOptions, testSQPOptimizer.getSettings());
      }
  
      @Test
      public void testParseOptimizationDataException() {
          // GIVEN
          final TestSQPOptimizer testSQPOptimizer = new TestSQPOptimizer();
          final EqualityConstraint equalityConstraint = new TestEqualityConstraints(100000);
          final TwiceDifferentiableFunction multivariateFunction = new RosenbrockFunction();
          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 100,000 >= 2",
                      exception.getMessage());
          }
      }
  
      @Test
      public void testParseOptimizationDataException2() {
          // GIVEN
          final TestSQPOptimizer testSQPOptimizer = new TestSQPOptimizer();
          final EqualityConstraint equalityConstraint = new TestEqualityConstraints(0);
          final TwiceDifferentiableFunction multivariateFunction = new RosenbrockFunction();
          final ObjectiveFunction objectiveFunction = new ObjectiveFunction(multivariateFunction);
          // WHEN
          try {
              testSQPOptimizer.parseOptimizationData(objectiveFunction, equalityConstraint);
              Assert.fail();
          } catch (final MathIllegalArgumentException exception) {
              Assert.assertEquals(LocalizedCoreFormats.ZERO_NOT_ALLOWED.getSourceString(),
                      exception.getMessage());
          }
      }
  
      private static class TestSQPOptimizer extends AbstractSQPOptimizer {
  
          TestSQPOptimizer() {
              super();
          }
  
          @Override
         protected LagrangeSolution doOptimize() {
             return null;
         }
     }
 
     private static class TestEqualityConstraints extends EqualityConstraint {
 
         private final int dimension;
 
         TestEqualityConstraints(final int dimension) {
             super(MatrixUtils.createRealVector(new double[dimension]));
             this.dimension = dimension;
         }
 
         @Override
         public int dim() {
             return dimension;
         }
 
         @Override
         public RealVector value(RealVector x) {
             return null;
         }
 
         @Override
         public RealMatrix jacobian(RealVector x) {
             return null;
         }
     }
 
 }