optimx v2018-7.10

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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.

Functions in optimx

Name Description
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.
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Vignettes of optimx

Name
Extend-optimx.Rmd
Extend-optimx.bib
Rvmmin.Rmd
Rvmmin.bib
SNewton.Rmd
SNewton.html
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Details

Date 2018-07-10
License GPL-2
LazyLoad Yes
NeedsCompilation no
VignetteBuilder knitr
Packaged 2018-07-10 16:08:14 UTC; john
Repository CRAN
Date/Publication 2018-09-30 14:50:05 UTC

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