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optextras (version 2016-8.8)

Tools to Support Optimization Possibly with 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.

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Version

Install

install.packages('optextras')

Monthly Downloads

260

Version

2016-8.8

License

GPL-2

Maintainer

John C. Nash

Last Published

August 8th, 2016

Functions in optextras (2016-8.8)

grback

Backward difference numerical gradient approximation.
kktchk

Check Kuhn Karush Tucker conditions for a supposed function minimum
scalechk

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

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

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

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

Perform axial search around a supposed minimum and provide diagnostics
grcentral

Central difference numerical gradient approximation.
gHgenb

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

Compute the maximum step along a search direction.
gHgen

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

Run tests, where possible, on user objective function
grchk

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

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

Forward difference numerical gradient approximation.