/*
    * Licensed to the Apache Software Foundation (ASF) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The ASF 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.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  package org.hipparchus.analysis.solvers;
  
  import org.hipparchus.analysis.QuinticFunction;
  import org.hipparchus.analysis.UnivariateFunction;
  import org.hipparchus.analysis.function.Expm1;
  import org.hipparchus.analysis.function.Sin;
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.util.FastMath;
  import org.junit.jupiter.api.Test;
  
  import static org.junit.jupiter.api.Assertions.assertEquals;
  import static org.junit.jupiter.api.Assertions.fail;
  
  /**
   * Test case for {@link MullerSolver2 Muller} solver.
   * <p>
   * Muller's method converges almost quadratically near roots, but it can
   * be very slow in regions far away from zeros. Test runs show that for
   * reasonably good initial values, for a default absolute accuracy of 1E-6,
   * it generally takes 5 to 10 iterations for the solver to converge.
   * <p>
   * Tests for the exponential function illustrate the situations where
   * Muller solver performs poorly.
   *
   */
  final class MullerSolver2Test {
      /**
       * Test of solver for the sine function.
       */
      @Test
      void testSinFunction() {
          UnivariateFunction f = new Sin();
          UnivariateSolver solver = new MullerSolver2();
          double min, max, expected, result, tolerance;
  
          min = 3.0; max = 4.0; expected = FastMath.PI;
          tolerance = FastMath.max(solver.getAbsoluteAccuracy(),
                      FastMath.abs(expected * solver.getRelativeAccuracy()));
          result = solver.solve(100, f, min, max);
          assertEquals(expected, result, tolerance);
  
          min = -1.0; max = 1.5; expected = 0.0;
          tolerance = FastMath.max(solver.getAbsoluteAccuracy(),
                      FastMath.abs(expected * solver.getRelativeAccuracy()));
          result = solver.solve(100, f, min, max);
          assertEquals(expected, result, tolerance);
      }
  
      /**
       * Test of solver for the quintic function.
       */
      @Test
      void testQuinticFunction() {
          UnivariateFunction f = new QuinticFunction();
          UnivariateSolver solver = new MullerSolver2();
          double min, max, expected, result, tolerance;
  
          min = -0.4; max = 0.2; expected = 0.0;
          tolerance = FastMath.max(solver.getAbsoluteAccuracy(),
                      FastMath.abs(expected * solver.getRelativeAccuracy()));
          result = solver.solve(100, f, min, max);
          assertEquals(expected, result, tolerance);
  
          min = 0.75; max = 1.5; expected = 1.0;
          tolerance = FastMath.max(solver.getAbsoluteAccuracy(),
                      FastMath.abs(expected * solver.getRelativeAccuracy()));
          result = solver.solve(100, f, min, max);
          assertEquals(expected, result, tolerance);
  
          min = -0.9; max = -0.2; expected = -0.5;
          tolerance = FastMath.max(solver.getAbsoluteAccuracy(),
                      FastMath.abs(expected * solver.getRelativeAccuracy()));
          result = solver.solve(100, f, min, max);
          assertEquals(expected, result, tolerance);
      }
  
      /**
       * Test of solver for the exponential function.
      * <p>
      * It takes 25 to 50 iterations for the last two tests to converge.
      */
     @Test
     void testExpm1Function() {
         UnivariateFunction f = new Expm1();
         UnivariateSolver solver = new MullerSolver2();
         double min, max, expected, result, tolerance;
 
         min = -1.0; max = 2.0; expected = 0.0;
         tolerance = FastMath.max(solver.getAbsoluteAccuracy(),
                     FastMath.abs(expected * solver.getRelativeAccuracy()));
         result = solver.solve(100, f, min, max);
         assertEquals(expected, result, tolerance);
 
         min = -20.0; max = 10.0; expected = 0.0;
         tolerance = FastMath.max(solver.getAbsoluteAccuracy(),
                     FastMath.abs(expected * solver.getRelativeAccuracy()));
         result = solver.solve(100, f, min, max);
         assertEquals(expected, result, tolerance);
 
         min = -50.0; max = 100.0; expected = 0.0;
         tolerance = FastMath.max(solver.getAbsoluteAccuracy(),
                     FastMath.abs(expected * solver.getRelativeAccuracy()));
         result = solver.solve(100, f, min, max);
         assertEquals(expected, result, tolerance);
     }
 
     /**
      * Test of parameters for the solver.
      */
     @Test
     void testParameters() {
         UnivariateFunction f = new Sin();
         UnivariateSolver solver = new MullerSolver2();
 
         try {
             // bad interval
             solver.solve(100, f, 1, -1);
             fail("Expecting MathIllegalArgumentException - bad interval");
         } catch (MathIllegalArgumentException ex) {
             // expected
         }
         try {
             // no bracketing
             solver.solve(100, f, 2, 3);
             fail("Expecting MathIllegalArgumentException - no bracketing");
         } catch (MathIllegalArgumentException ex) {
             // expected
         }
     }
 }