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## de.jstacs.algorithms.optimization
Class NumericalDifferentiableFunction

    java.lang.Object
      de.jstacs.algorithms.optimization.DifferentiableFunction
          de.jstacs.algorithms.optimization.NumericalDifferentiableFunction

All Implemented Interfaces:
  Function

    public abstract class NumericalDifferentiableFunction

extends DifferentiableFunction

This class is the framework for any numerical differentiable function `f: R^n -> R`. The gradient is computed numerically. Each partial differentiation is computed by the formula
`\partial_k f(x) = \frac{f(x)-f(x+\varepsilon*e_k)}{\varepsilon}`.

Author:
  Jens Keilwagen

Field Summary
`protected double` | `eps` The constant used in the computation of the gradient.

Constructor Summary
`NumericalDifferentiableFunction(double epsilon)` Sets the value for epsilon for this `NumericalDifferentiableFunction`.

Method Summary
`double[]` | `evaluateGradientOfFunction(double[] x)` Evaluates the gradient of a function at a certain vector (in mathematical sense) `x` numerically.

Methods inherited from class de.jstacs.algorithms.optimization.DifferentiableFunction
`findOneDimensionalMin`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Methods inherited from interface de.jstacs.algorithms.optimization.Function
`evaluateFunction, getDimensionOfScope`

Field Detail

### eps

    protected double eps

  The constant used in the computation of the gradient. Should be close to 0 but not exactly 0.

Constructor Detail

### NumericalDifferentiableFunction

    public NumericalDifferentiableFunction(double epsilon)
                                    throws IllegalArgumentException

  Sets the value for epsilon for this `NumericalDifferentiableFunction`.

  Parameters:
    `epsilon` - the epsilon used for the numerical differentiation
  Throws:
    `IllegalArgumentException` - if `epsilon = 0`

Method Detail

### evaluateGradientOfFunction

    public double[] evaluateGradientOfFunction(double[] x)
                                        throws DimensionException,
                                               EvaluationException

  Evaluates the gradient of a function at a certain vector (in mathematical sense) `x` numerically.

  Specified by:
    `evaluateGradientOfFunction` in class `DifferentiableFunction`
  Parameters:
    `x` - the current vector
  Returns:
    the evaluation of the gradient of a function, has dimension `Function.getDimensionOfScope()`
  Throws:
    `DimensionException` - if `dim(x) != n`, with
    `EvaluationException` - if there was something wrong during the evaluation of the gradient
  See Also:
    `DifferentiableFunction.evaluateGradientOfFunction(double[])`

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