1 /*
2 * Licensed to the Hipparchus project 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 Hipparchus project 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 package org.hipparchus.optim.nonlinear.vector.constrained;
18
19
20
21 import org.hipparchus.analysis.MultivariateFunction;
22 import org.hipparchus.linear.RealMatrix;
23 import org.hipparchus.linear.RealVector;
24 import org.hipparchus.linear.ArrayRealVector;
25
26 /** A MultivariateFunction that also has a defined gradient and Hessian.
27 * @since 3.1
28 */
29 public abstract class TwiceDifferentiableFunction implements MultivariateFunction {
30 /**
31 * Returns the dimensionality of the function domain.
32 * If dim() returns (n) then this function expects an n-vector as its input.
33 * @return the expected dimension of the function's domain
34 */
35 public abstract int dim();
36
37 /**
38 * Returns the value of this function at (x)
39 *
40 * @param x a point to evaluate this function at.
41 * @return the value of this function at (x)
42 */
43 public abstract double value(RealVector x);
44
45 /**
46 * Returns the gradient of this function at (x)
47 *
48 * @param x a point to evaluate this gradient at
49 * @return the gradient of this function at (x)
50 */
51 public abstract RealVector gradient(RealVector x);
52
53 /**
54 * The Hessian of this function at (x)
55 *
56 * @param x a point to evaluate this Hessian at
57 * @return the Hessian of this function at (x)
58 */
59 public abstract RealMatrix hessian(RealVector x);
60
61 /**
62 * Returns the value of this function at (x)
63 *
64 * @param x a point to evaluate this function at.
65 * @return the value of this function at (x)
66 */
67 @Override
68 public double value(final double[] x) {
69 return value(new ArrayRealVector(x, false));
70 }
71
72 /**
73 * Returns the gradient of this function at (x)
74 *
75 * @param x a point to evaluate this gradient at
76 * @return the gradient of this function at (x)
77 */
78 public RealVector gradient(final double[] x) {
79 return gradient(new ArrayRealVector(x, false));
80 }
81
82 /**
83 * The Hessian of this function at (x)
84 *
85 * @param x a point to evaluate this Hessian at
86 * @return the Hessian of this function at (x)
87 */
88 public RealMatrix hessian(final double[] x) {
89 return hessian(new ArrayRealVector(x, false));
90 }
91 }