optimx-package | R Documentation |

`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.

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 scalechk Check scale of initial parameters and bounds optsp An environment to hold some globally useful items used by optimization programs proptimr compact output of optimr() result object

John C Nash <nashjc@uottawa.ca> and Ravi Varadhan <RVaradhan@jhmi.edu>

Maintainer: John C Nash <nashjc@uottawa.ca>

Nash, John C. and Varadhan, Ravi (2011) Unifying Optimization Algorithms to Aid Software System Users: optimx for R, Journal of Statistical Software, publication pending.

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