math.function.NumericallyDiffMultivariateFunction Maven / Gradle / Ivy
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/*
* Copyright 2013 SPZ
*
* Licensed 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
*
* http://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.
*/
package math.function;
import math.MathConsts;
/**
* Finite difference numerical gradient calculation using the forward difference
* approximation {@code f'(x) = (f(x + h) - f(x)) / h}.
*
* Scaling of {@code h} is taken into account by each individual {@code h} being
* based upon the absolute magnitude of the corresponding element in the vector
* {@code x}.
*/
public abstract class NumericallyDiffMultivariateFunction implements
DiffMultivariateFunction {
protected final double diffScale;
public NumericallyDiffMultivariateFunction() {
this(1.5 * Math.sqrt(MathConsts.MACH_EPS));
}
public NumericallyDiffMultivariateFunction(double diffScale) {
this.diffScale = diffScale;
}
@Override
public final void derivativeAt(double[] x, double[] grad) {
double fx = this.valueAt(x);
for (int i = 0; i < x.length; ++i) {
double xi = x[i];
double hi = (xi != 0.0) ? diffScale * Math.abs(xi) : diffScale;
double xi_plus_hi = xi + hi;
// account for potential round-off errors
hi = xi_plus_hi - xi;
x[i] = xi_plus_hi;
// new function value for advance in variable i
double fx_plus_hi = this.valueAt(x);
// estimated gradient component for variable i
grad[i] = (fx_plus_hi - fx) / hi;
// restore the old value for variable i
x[i] = xi;
}
}
}