jsat.math.optimization

Interface Optimizer2

All Known Implementing Classes:

BFGS, LBFGS, ModifiedOWLQN

public interface Optimizer2

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 and 2nd derivative may be null.

Author:

Edward Raff

Method Summary

Modifier and Type	Method and Description
Optimizer2	clone()
int	getMaximumIterations() Returns the maximum number of iterations to perform
void	optimize(double tolerance, Vec w, Vec x0, Function f, FunctionVec fp, FunctionVec fpp) 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, FunctionVec fpp, ExecutorService ex) 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

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

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

fpp - the Hessian of the objective function, may be null depending on the optimizer

optimize

void optimize(double tolerance,
              Vec w,
              Vec x0,
              Function f,
              FunctionVec fp,
              FunctionVec fpp,
              ExecutorService ex)

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

fpp - the Hessian of the objective function, may be null depending on the optimizer

ex - the source of threads for parallel computation, may be null to perform serial computation.

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

Optimizer2 clone()