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java.lang.Object jebl.math.NumericalDerivative
public class NumericalDerivative
approximates numerically the first and second derivatives of a function of a single variable and approximates gradient and diagonal of Hessian for multivariate functions
Constructor Summary | |
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NumericalDerivative()
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Method Summary | |
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static double[] |
diagonalHessian(MultivariateFunction f,
double[] x)
determine diagonal of Hessian |
static double |
firstDerivative(UnivariateFunction f,
double x)
determine first derivative |
static double[] |
gradient(MultivariateFunction f,
double[] x)
determine gradient |
static void |
gradient(MultivariateFunction f,
double[] x,
double[] grad)
determine gradient |
static double |
secondDerivative(UnivariateFunction f,
double x)
determine second derivative |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public NumericalDerivative()
Method Detail |
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public static double firstDerivative(UnivariateFunction f, double x)
f
- univariate functionx
- argument
public static double secondDerivative(UnivariateFunction f, double x)
f
- univariate functionx
- argument
public static double[] gradient(MultivariateFunction f, double[] x)
f
- multivariate functionx
- argument vector
public static void gradient(MultivariateFunction f, double[] x, double[] grad)
f
- multivariate functionx
- argument vectorgrad
- vector for gradientpublic static double[] diagonalHessian(MultivariateFunction f, double[] x)
f
- multivariate functionx
- argument vector
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