optimx
provides a replacement and extension of the link{optim()} function to
unify and streamline optimization capabilities in R for smooth, possibly box
constrained functions of several or many parameters
The three functions ufn, ugr and uhess wrap corresponding user functions fn, gr, and
hess so that these functions can be executed safely (via try()) and also so parameter or
function scaling can be applied. The wrapper
functions also allow for maximization of functions (via minimization of the negative of
the function) using the logical parameter maximize.
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.
John C Nash <nashjc@uottawa.ca> and Ravi Varadhan <RVaradhan@jhmi.edu>
Maintainer: John C Nash <nashjc@uottawa.ca>
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.
checksolver Checks if method is available in allmeth
ctrldefault Sets the default values of elements of the control() list
dispdefault To display default control settings
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}
grpracma Gradient approximation using \code{pracma}
hesschk Check that Hessian function evaluation
matches numerical approximation
hjn A didactic example code of the Hooke and Jeeves algorithm
kktchk Check the Karush-Kuhn-Tucker optimality conditions
multistart Try a single method with multiple starting parameter sets
ncg Revised CG solver
nvm Revised Variable Metric solver
opm Wrapper that allows multiple minimizers to be applied to a
given objective function
optchk Check supplied objective function
optimr Wrapper that allows different (single) minimizers to be
applied to a given objective function using a common syntax
like that of optim()
optimx Wrapper that allows multiple minimizers to be applied to a
given objective function. Complexity of the code maked this
function difficult to maintain, and opm() is the suggested
replacement, but optimx() is retained for backward
compatibility.
optimx.check a component of optimx()
optimx-package a component of optimx()
optimx.run a component of optimx()
optimx.setup a component of optimx()
optsp An environment to hold some globally useful items
used by optimization programs. Created on loading package
with zzz.R
polyopt Allows sequential application of methods to a given problem.
proptimr compact output of optimr() result object
Rcgmin Conjugate gradients minimization
Rcgminb Bounds constrained conjugate gradients minimization
Rcgminu Unconstrained conjugate gradients minimization
Rtnmin-package Internal functions for the S.G.Nash truncated newton method
Rvmmin Variable metric minimization method
Rvmminb Bounds constrained variable metric minimization method
Rvmminu Unconstrained variable metric minimization method
scalechk Check scale of initial parameters and bounds
snewtm Demonstration Newton-Marquardt minimization method
snewton Demonstration safeguarded Newton minimization method
snewtonmb Bounds constrained safeguarded Newton method
tnbc Bounds constrained truncated Newton method
tn Unconstrained truncated Newton method
Nash, John C. and Varadhan, Ravi (2011) Unifying Optimization Algorithms to Aid Software System Users: optimx for R, Journal of Statistical Software, publication pending.