Provides tools that work with extensions of the optim() function to unify and streamline optimization capabilities in R for smooth, possibly box constrained functions of several or many parameters
There are three test functions, fnchk, grchk, and hesschk, to allow the user
function to be tested for validity and correctness. However, no set of tests is
exhaustive, and extensions and improvements are welcome. The package
numDeriv is used for generation of numerical approximations to
derivatives.
| Package: | optextras |
| Version: | 2012-6.18 |
| Date: | 2012-06-18 |
| License: | GPL-2 |
| Lazyload: | Yes |
| Depends: | numDeriv |
| Suggests: | BB, ucminf, Rcgmin, Rvmmin, minqa, setRNG, dfoptim |
| Repository: | R-Forge |
| Repository/R-Forge/Project: | optimizer |
Index:
axsearch Perform an axial search optimality check
bmchk Check bounds and masks for parameter constraints
bmstep Compute the maximum step along a search direction.
fnchk Test validity of user function
gHgen Compute gradient and Hessian as a given
set of parameters
gHgenb Compute gradient and Hessian as a given
set of parameters appying bounds and masks
grback Backward numerical gradient approximation
grcentral Central numerical gradient approximation
grchk Check that gradient function evaluation
matches numerical gradient
grfwd Forward numerical gradient approximation
grnd Gradient approximation using \code{numDeriv}
hesschk Check that Hessian function evaluation
matches numerical approximation
kktchk Check the Karush-Kuhn-Tucker optimality conditions
optsp An environment to hold some globally useful items
used by optimization programs
scalechk Check scale of initial parameters and bounds
Nash, John C. and Varadhan, Ravi (2011) Unifying Optimization Algorithms to Aid Software System Users: optimx for R, Journal of Statistical Software, publication pending.
optim