jsat.math.optimization

Interface Optimizer

All Known Implementing Classes:

BFGS, LBFGS, ModifiedOWLQN, NelderMead

public interface Optimizer

This interface defines a contract for multivariate function minimization.

Different optimization methods will use or require different amounts of information. Depending on the optimizer, the 1st derivative may not be necessary and can be null.

Method Summary

Modifier and Type	Method and Description
Optimizer	clone()
int	getMaximumIterations() Returns the maximum number of iterations to perform
default void	optimize(double tolerance, Vec w, Vec x0, Function f, FunctionVec fp) Attempts to optimize the given function by finding the value of w that will minimize the value returned by f(w), using w = x0 as an initial starting point.
void	optimize(double tolerance, Vec w, Vec x0, Function f, FunctionVec fp, boolean parallel) Attempts to optimize the given function by finding the value of w that will minimize the value returned by f(w), using w = x0 as an initial starting point.
void	setMaximumIterations(int iterations) Sets the maximum number of iterations allowed for the optimization method

Method Detail

optimize

default void optimize(double tolerance,
                      Vec w,
                      Vec x0,
                      Function f,
                      FunctionVec fp)

Attempts to optimize the given function by finding the value of w that will minimize the value returned by f(w), using w = x₀ as an initial starting point.

Parameters:

tolerance - the value that the gradient norm must be less than to consider converged

w - the the location to store the final solution

x0 - the initial guess for the solution. This value will not be changed, and intermediate matrices will be created as the same type.

f - the objective function to minimizer

fp - the derivative of the objective function, may be null depending on the optimizer

optimize

void optimize(double tolerance,
              Vec w,
              Vec x0,
              Function f,
              FunctionVec fp,
              boolean parallel)

Attempts to optimize the given function by finding the value of w that will minimize the value returned by f(w), using w = x₀ as an initial starting point.

Parameters:

tolerance - the value that the gradient norm must be less than to consider converged

w - the the location to store the final solution

x0 - the initial guess for the solution. This value will not be changed, and intermediate matrices will be created as the same type.

f - the objective function to minimizer

fp - the derivative of the objective function, may be null depending on the optimizer

parallel - true if multiple threads should be used for optimization, or false if a single thread should be used.

setMaximumIterations

void setMaximumIterations(int iterations)

Sets the maximum number of iterations allowed for the optimization method

Parameters:

iterations - the maximum number of iterations to perform

getMaximumIterations

int getMaximumIterations()

Returns the maximum number of iterations to perform

Returns:

the maximum number of iterations to perform

clone

Optimizer clone()