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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    *      http://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  package org.apache.commons.math3.optimization.general;
19  
20  /**
21   * This interface represents a preconditioner for differentiable scalar
22   * objective function optimizers.
23   * @deprecated As of 3.1 (to be removed in 4.0).
24   * @since 2.0
25   */
26  @Deprecated
27  public interface Preconditioner {
28      /**
29       * Precondition a search direction.
30       * <p>
31       * The returned preconditioned search direction must be computed fast or
32       * the algorithm performances will drop drastically. A classical approach
33       * is to compute only the diagonal elements of the hessian and to divide
34       * the raw search direction by these elements if they are all positive.
35       * If at least one of them is negative, it is safer to return a clone of
36       * the raw search direction as if the hessian was the identity matrix. The
37       * rationale for this simplified choice is that a negative diagonal element
38       * means the current point is far from the optimum and preconditioning will
39       * not be efficient anyway in this case.
40       * </p>
41       * @param point current point at which the search direction was computed
42       * @param r raw search direction (i.e. opposite of the gradient)
43       * @return approximation of H<sup>-1</sup>r where H is the objective function hessian
44       */
45      double[] precondition(double[] point, double[] r);
46  }