edu.jhu.bme.smile.commons.optimize.test
Class FunctionRosenbrock

java.lang.Object
  edu.jhu.bme.smile.commons.optimize.test.FunctionRosenbrock

All Implemented Interfaces:

OptimizableNDContinuous, OptimizableNDContinuousDifferentiable

public class FunctionRosenbrock
extends java.lang.Object
implements OptimizableNDContinuousDifferentiable

Test case to test ND optimizers. Function is 5*(x-y^2)^2 + (1-y)^2

Author:

Yufeng Guo, Hanlin Wan

Constructor Summary

FunctionRosenbrock()
Default constructor.

FunctionRosenbrock(double[] domMin, double[] domMax)
Constructor to set the domain.

Method Summary

double[][]	get2ndDerivative(double[] x) Gets the hessian of the function.
double[][]	get2ndDerivative(double[] x, double step, int method) Gets the hessian of the function.
double	get2ndDerivative(double[] x, int dim1, int dim2) Gets the 2nd derivative of the function in a specific direction.
double[]	getDerivative(double[] x) Gets the gradient of the function.
double[]	getDerivative(double[] x, double step, int method) Gets the gradient of the function.
double[]	getDomainMax() Gets the domain maximum.
double[]	getDomainMin() Gets the domain minimum.
double	getDomainTolerance() Gets the tolerance.
int	getNumberOfDimensions() Gets the number of dimensions.
double	getValue(double[] x) Evaluates the function at location x.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FunctionRosenbrock

public FunctionRosenbrock()

Default constructor.

FunctionRosenbrock

public FunctionRosenbrock(double[] domMin,
                          double[] domMax)

Constructor to set the domain.

Parameters:

domMin - domain minimum

domMax - domain maximum

Method Detail

getDomainMax

public double[] getDomainMax()

Gets the domain maximum.

Specified by:

getDomainMax in interface OptimizableNDContinuous

Returns:

domain max

getDomainMin

public double[] getDomainMin()

Gets the domain minimum.

Specified by:

getDomainMin in interface OptimizableNDContinuous

Returns:

domain min

getDomainTolerance

public double getDomainTolerance()

Gets the tolerance.

Specified by:

getDomainTolerance in interface OptimizableNDContinuous

Returns:

tolerance

getNumberOfDimensions

public int getNumberOfDimensions()

Gets the number of dimensions.

Specified by:

getNumberOfDimensions in interface OptimizableNDContinuous

Returns:

number of dimensions

getValue

public double getValue(double[] x)

Evaluates the function at location x.

Specified by:

getValue in interface OptimizableNDContinuous

Parameters:

x - location to evaluate function

Returns:

value of the function at x

get2ndDerivative

public double[][] get2ndDerivative(double[] x)

Gets the hessian of the function. Uses the default step size and method

Specified by:

get2ndDerivative in interface OptimizableNDContinuousDifferentiable

Parameters:

x - location to calculate hessian

Returns:

hessian at location x

get2ndDerivative

public double[][] get2ndDerivative(double[] x,
                                   double step,
                                   int method)

Gets the hessian of the function. Uses the specified step size and method.

Parameters:

x - location to calculate hessian

step - step size

method - method

Returns:

hessian at location x

get2ndDerivative

public double get2ndDerivative(double[] x,
                               int dim1,
                               int dim2)

Gets the 2nd derivative of the function in a specific direction. Uses the default step size and method.

Specified by:

get2ndDerivative in interface OptimizableNDContinuousDifferentiable

Parameters:

x - location to calculate derivative

dim1 - dimension to take the first derivative

dim2 - dimension to take the second derivative

Returns:

2nd derivative at location x in the specified direction

getDerivative

public double[] getDerivative(double[] x)

Gets the gradient of the function. Uses the default step size and method

Specified by:

getDerivative in interface OptimizableNDContinuousDifferentiable

Parameters:

x - location to calculate gradient

Returns:

gradient at location x

getDerivative

public double[] getDerivative(double[] x,
                              double step,
                              int method)

Gets the gradient of the function. Uses the specified step size and method.

Parameters:

x - location to calculate gradient

step - step size

method - method

Returns:

gradient at location x