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1   /*
2    * Licensed to the Apache Software Foundation (ASF) 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 ASF 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  
18  /*
19   * This is not the original file distributed by the Apache Software Foundation
20   * It has been modified by the Hipparchus project
21   */
22  package org.hipparchus.optim.nonlinear.vector.leastsquares;
23  
24  /**
25   * An algorithm that can be applied to a non-linear least squares problem.
26   */
27  public interface LeastSquaresOptimizer {
28  
29      /**
30       * Solve the non-linear least squares problem.
31       *
32       *
33       * @param leastSquaresProblem the problem definition, including model function and
34       *                            convergence criteria.
35       * @return The optimum.
36       */
37      Optimum optimize(LeastSquaresProblem leastSquaresProblem);
38  
39      /**
40       * The optimum found by the optimizer. This object contains the point, its value, and
41       * some metadata.
42       */
43      interface Optimum extends LeastSquaresProblem.Evaluation {
44  
45          /**
46           * Get the number of times the model was evaluated in order to produce this
47           * optimum.
48           *
49           * @return the number of model (objective) function evaluations
50           */
51          int getEvaluations();
52  
53          /**
54           * Get the number of times the algorithm iterated in order to produce this
55           * optimum. In general least squares it is common to have one {@link
56           * #getEvaluations() evaluation} per iterations.
57           *
58           * @return the number of iterations
59           */
60          int getIterations();
61  
62          /**
63           * Create a new optimum from an evaluation and the values of the counters.
64           *
65           * @param value       the function value
66           * @param evaluations number of times the function was evaluated
67           * @param iterations  number of iterations of the algorithm
68           * @return a new optimum based on the given data.
69           */
70          static Optimum of(final LeastSquaresProblem.Evaluation value,
71                            final int evaluations,
72                            final int iterations) {
73              return new OptimumImpl(value, evaluations, iterations);
74          }
75  
76      }
77  
78  }