package edu.stanford.nlp.optimization;
/**
* The interface for unconstrained function minimizers.
*
* Implementations may also vary in their requirements for the
* arguments. For example, implementations may or may not care if the
* {@code initial} feasible vector turns out to be non-feasible
* (or {@code null}!). Similarly, some methods may insist that objectives
* and/or constraint {@code Function} objects actually be
* {@code DiffFunction} objects.
*
* @author <a href="mailto:klein@cs.stanford.edu">Dan Klein</a>
* @version 1.0
* @since 1.0
*/
public interface Minimizer<T extends Function> {
/**
* Attempts to find an unconstrained minimum of the objective
* {@code function} starting at {@code initial}, accurate to
* within {@code functionTolerance} (normally implemented as
* a multiplier of the range value to give range tolerance).
*
* @param function The objective function
* @param functionTolerance A {@code double} value
* @param initial An initial feasible point
* @return Unconstrained minimum of function
*/
double[] minimize(T function, double functionTolerance, double[] initial);
/**
* Attempts to find an unconstrained minimum of the objective
* {@code function} starting at {@code initial}, accurate to
* within {@code functionTolerance} (normally implemented as
* a multiplier of the range value to give range tolerance), but
* running only for at most {@code maxIterations} iterations.
*
* @param function The objective function
* @param functionTolerance A {@code double} value
* @param initial An initial feasible point
* @param maxIterations Maximum number of iterations
* @return Unconstrained minimum of function
*/
double[] minimize(T function, double functionTolerance, double[] initial, int maxIterations);
}