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optimx

Expanded Replacement and Extension of the 'optim' Function

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.

Functions (36)

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.

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.

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.

Rvmminb

Variable metric nonlinear function minimization with bounds constraints

Rvmminu

Variable metric nonlinear function minimization, unconstrained

axsearch

Perform axial search around a supposed minimum and provide diagnostics

bmchk

Check bounds and masks for parameter constraints used in nonlinear optimization

bmstep

Compute the maximum step along a search direction.

checksolver

Test if requested solver is present

coef.opm

Summarize opm object

ctrldefault

set control defaults

fnchk

Run tests, where possible, on user objective function

gHgen

Generate gradient and Hessian for a function at given parameters.

gHgenb

Generate gradient and Hessian for a function at given parameters.

grback

Backward difference numerical gradient approximation.

grcentral

Central difference numerical gradient approximation.

grchk

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

grfwd

Forward difference numerical gradient approximation.

grnd

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

hesschk

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

hjn

Compact R Implementation of Hooke and Jeeves Pattern Search Optimization

kktchk

Check Kuhn Karush Tucker conditions for a supposed function minimum

multistart

General-purpose optimization - multiple starts

opm

General-purpose optimization

optchk

General-purpose optimization

optimr

General-purpose optimization

optimx

General-purpose optimization

optimx-package

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

polyopt

General-purpose optimization - sequential application of methods

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.

summary.optimx

Summarize optimx object

Truncated Newton minimization of an unconstrained function.

tnbc

Truncated Newton function minimization with bounds constraints

optimx

Expanded Replacement and Extension of the 'optim' Function

v2020-4.2

GPL-2

Authors

John C Nash [aut, cre], Ravi Varadhan [aut], Gabor Grothendieck [ctb]

Initial release

2020-04-02

optimx

Functions (36)

optimx

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