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optimx (version 2018-7.10)

Expanded Replacement and Extension of the 'optim' Function

Description

Provides a replacement and extension of the optim() function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that function 'optimr()' was prepared to simplify the incorporation of minimization codes going forward. Also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here.

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Version

Install

install.packages('optimx')

Monthly Downloads

17,678

Version

2018-7.10

License

GPL-2

Maintainer

John C. Nash

Last Published

September 30th, 2018

Functions in optimx (2018-7.10)

Rcgminb

An R implementation of a bounded nonlinear conjugate gradient algorithm with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22 for its main structure. CALL THIS VIA Rcgmin AND DO NOT USE DIRECTLY.
grcentral

Central difference numerical gradient approximation.
coef

Summarize opm object
ctrldefault

set control defaults
polyopt

General-purpose optimization - sequential application of methods
scalechk

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

Compact display of an optimr() result object
optimx

General-purpose optimization
grnd

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

Forward difference numerical gradient approximation.
grchk

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

Check Kuhn Karush Tucker conditions for a supposed function minimum
opm

General-purpose optimization
fnchk

Run tests, where possible, on user objective function
multistart

General-purpose optimization - multiple starts
tn

Truncated Newton minimization of an unconstrained function.
optchk

General-purpose optimization
gHgenb

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

Truncated Newton function minimization with bounds constraints
gHgen

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

Backward difference numerical gradient approximation.
snewton

Safeguarded Newton methods for function minimization using R functions.
hesschk

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

General-purpose optimization
optimx-package

A replacement and extension of the optim() function, plus various optimization tools
summary.optimx

Summarize optimx object
hjn

Compact R Implementation of Hooke and Jeeves Pattern Search Optimization
Rvmminb

Variable metric nonlinear function minimization with bounds constraints
checksolver

Test if requested solver is present
bmstep

Compute the maximum step along a search direction.
Rvmminu

Variable metric nonlinear function minimization, unconstrained
Rcgminu

An R implementation of an unconstrained nonlinear conjugate gradient algorithm with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22 for its main structure. CALL THIS VIA Rcgmin AND DO NOT USE DIRECTLY.
axsearch

Perform axial search around a supposed minimum and provide diagnostics
Rcgmin

An R implementation of a nonlinear conjugate gradient algorithm with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22 for its main structure.
bmchk

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

Variable metric nonlinear function minimization, driver.