Learn R Programming

⚠️There's a newer version (2019-12.4) of this package.Take me there.

optextras (version 2013-10.27)

A set of tools to support optimization methods (function minimization with at most bounds and masks).

Description

Tools to assist in safely applying user generated objective and derivative function to optimization programs. These are primarily function minimization methods with at most bounds and masks on the parameters. Provides a way to check the basic computation of objective functions that the user provides, along with proposed gradient and Hessian functions, as well as to wrap such functions to avoid failures when inadmissible parameters are provided. Check bounds and masks. Check scaling or optimality conditions. Perform an axial search to seek lower points on the objective function surface. Includes forward, central and backward gradient approximation codes.

Copy Link

Version

Install

install.packages('optextras')

Monthly Downloads

262

Version

2013-10.27

License

GPL-2

Maintainer

John C. Nash

Last Published

November 8th, 2013

Functions in optextras (2013-10.27)

ufn

Wrap user objective function for optimization tools
gHgenb

Generate gradient and Hessian for a function at given parameters.
uhess

Wrapper for user Hessian function for optimization tools
axsearch

Perform axial search around a supposed minimum and provide diagnostics
grcentral

Central difference numerical gradient approximation.
ugHgenb

Generate gradient and Hessian for a function at given parameters using function wrappers to control for scaling and inadmissible inputs.
scalecheck

Check the scale of the initial parameters and bounds input to an optimization code used in nonlinear optimization
kktc

Check Kuhn Karush Tucker conditions for a supposed function minimum
fnchk

Run tests, where possible, on user objective function
gHgen

Generate gradient and Hessian for a function at given parameters.
grback

Backward difference numerical gradient approximation.
bmstep

Compute the maximum step along a search direction.
hesschk

Run tests, where possible, on user objective function and (optionally) gradient and hessian
ugr

Wrapper for user gradient function for optimization tools
grchk

Run tests, where possible, on user objective function and (optionally) gradient and hessian
grfwd

Forward difference numerical gradient approximation.
optextras-package

A replacement and extension of the optim() function, plus various optimization tools
bmchk

Check bounds and masks for parameter constraints used in nonlinear optimization
grnd

A reorganization of the call to numDeriv grad() function.