|FunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>||
Interface for unconstrained minimization of nonlinear functions.
|AbstractAnytimeFunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>||
A partial implementation of a minimization algorithm that is iterative, stoppable, and approximate.
Implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton nonlinear minimization algorithm.
Conjugate gradient method is a class of algorithms for finding the unconstrained local minimum of a nonlinear function.
Implementation of the Davidon-Fletcher-Powell (DFP) formula for a Quasi-Newton minimization update.
Implementation of the derivative-free unconstrained nonlinear direction-set minimization algorithm called "Powell's Method" by Numerical Recipes.
This is an implementation of the Fletcher-Reeves conjugate gradient minimization procedure.
This is an implementation of the classic Gradient Descent algorithm, also known as Steepest Descent, Backpropagation (for neural nets), or Hill Climbing.
This is an implementation of the Liu-Storey conjugate gradient minimization procedure.
Implementation of the Downhill Simplex minimization algorithm, also known as the Nelder-Mead method.
This is an implementation of the Polack-Ribiere conjugate gradient minimization procedure.
This is an abstract implementation of the Quasi-Newton minimization method, sometimes called "Variable-Metric methods." This family of minimization algorithms uses first-order gradient information to find a locally minimum to a scalar function.
Implementation of almost zero-gradient convergence test for function minimizers.