# Expanded Replacement and Extension of the 'optim' Function

## Documentation for package ‘optimx’ version 2023-10.21

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

optimx-package | A replacement and extension of the optim() function, plus various optimization tools
as.data.frame.optimx | General-purpose optimization
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.
checkallsolvers | Test if requested solver is present
checksolver | Test if requested solver is present
coef.opm | Summarize opm object
coef.optimx | Summarize opm object
coef<- | Summarize opm object
coef<-.opm | Summarize opm object
coef<-.optimx | Summarize opm object
ctrldefault | set control defaults
dispdefault | 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.
grpracma | 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
ncg | 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.
ncgqs | 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.
nvm | Variable metric nonlinear function minimization, driver.
opm | General-purpose optimization
optchk | General-purpose optimization
optimr | General-purpose optimization
optimx | General-purpose optimization
optsp | Forward difference numerical gradient approximation.
polyopt | General-purpose optimization - sequential application of methods
proptimr | Compact display of an 'optimr()' result object
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
scalechk | Check the scale of the initial parameters and bounds input to an optimization code used in nonlinear optimization
snewtm | Safeguarded Newton methods for function minimization using R functions.
snewton | Safeguarded Newton methods for function minimization using R functions.
summary.optimx | Summarize optimx object
tn | Truncated Newton minimization of an unconstrained function.
tnbc | Truncated Newton function minimization with bounds constraints
[.optimx | General-purpose optimization