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optimx (version 2023-8.13)

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. This is the version for CRAN.

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Version

Install

install.packages('optimx')

Monthly Downloads

24,057

Version

2023-8.13

License

GPL-2

Maintainer

John C. Nash

Last Published

August 14th, 2023

Functions in optimx (2023-8.13)

bmstep

Compute the maximum step along a search direction.
Rvmminb

Variable metric nonlinear function minimization with bounds constraints
checksolver

Test if requested solver is present
Rvmminu

Variable metric nonlinear function minimization, unconstrained
axsearch

Perform axial search around a supposed MINIMUM and provide diagnostics
fnchk

Run tests, where possible, on user objective function
gHgen

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

General-purpose optimization
optimr

General-purpose optimization
gHgenb

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

Backward difference numerical gradient approximation.
multistart

General-purpose optimization - multiple starts
bmchk

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

Central difference numerical gradient approximation.
grchk

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

General-purpose optimization
optimx-package

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

General-purpose optimization
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.
summary.optimx

Summarize optimx object
tn

Truncated Newton minimization of an unconstrained function.
grfwd

Forward difference numerical gradient approximation.
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.
grpracma

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

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

Variable metric nonlinear function minimization, driver.
hjn

Compact R Implementation of Hooke and Jeeves Pattern Search Optimization
hesschk

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

Check Kuhn Karush Tucker conditions for a supposed function minimum
coef

Summarize opm object
ctrldefault

set control defaults
polyopt

General-purpose optimization - sequential application of methods
tnbc

Truncated Newton function minimization with bounds constraints
proptimr

Compact display of an optimr() result object
scalechk

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

Safeguarded Newton methods for function minimization using R functions.