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optimx (version 2020-4.2)

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

17,678

Version

2020-4.2

License

GPL-2

Maintainer

John C. Nash

Last Published

April 8th, 2020

Functions in optimx (2020-4.2)

Compute the maximum step along a search direction.

Test if requested solver is present

Perform axial search around a supposed minimum and provide diagnostics

Variable metric nonlinear function minimization with bounds constraints

Central difference numerical gradient approximation.

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.

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.

Summarize opm object

Variable metric nonlinear function minimization, unconstrained

set control defaults

Variable metric nonlinear function minimization, driver.

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.

Check bounds and masks for parameter constraints used in nonlinear optimization

Backward difference numerical gradient approximation.

Generate gradient and Hessian for a function at given parameters.

Forward difference numerical gradient approximation.

General-purpose optimization

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

Run tests, where possible, on user objective function

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

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

General-purpose optimization

General-purpose optimization

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

Generate gradient and Hessian for a function at given parameters.

Safeguarded Newton methods for function minimization using R functions.

Compact R Implementation of Hooke and Jeeves Pattern Search Optimization

Compact display of an optimr() result object

Summarize optimx object

General-purpose optimization - sequential application of methods

General-purpose optimization - multiple starts

General-purpose optimization

Check Kuhn Karush Tucker conditions for a supposed function minimum

Truncated Newton minimization of an unconstrained function.

Truncated Newton function minimization with bounds constraints

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