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.MultivariateVectorFunction;
22 import org.hipparchus.linear.ArrayRealVector;
23 import org.hipparchus.linear.RealMatrix;
24 import org.hipparchus.linear.RealVector;
25
26 /** A MultivariateFunction that also has a defined gradient and Hessian.
27 * @since 3.1
28 */
29 public interface VectorDifferentiableFunction extends MultivariateVectorFunction {
30
31 /**
32 * Returns the dimensionality of the function domain.
33 * If dim() returns (n) then this function expects an n-vector as its input.
34 * @return the expected dimension of the function's domain
35 */
36 int dim();
37
38 /**
39 * Returns the dimensionality of the function eval.
40 *
41 * @return the expected dimension of the function's eval
42 */
43 int dimY();
44
45 /**
46 * Returns the value of this function at (x)
47 *
48 * @param x a point to evaluate this function at.
49 * @return the value of this function at (x)
50 */
51 RealVector value(RealVector x);
52
53 /**
54 * Returns the value of this function at (x)
55 *
56 * @param x a point to evaluate this function at.
57 * @return the value of this function at (x)
58 */
59 @Override
60 default double[] value(final double[] x) {
61 return value(new ArrayRealVector(x, false)).toArray();
62 }
63
64 /**
65 * Returns the gradient of this function at (x)
66 *
67 * @param x a point to evaluate this gradient at
68 * @return the gradient of this function at (x)
69 */
70 RealMatrix jacobian(RealVector x);
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 default RealMatrix gradient(final double[] x) {
79 return jacobian(new ArrayRealVector(x, false));
80 }
81
82 }