R: A replacement and extension of the optim() function, plus...

optextras-package {optextras} | R Documentation

## A replacement and extension of the optim() function, plus various optimization tools

### Description

Provides a replacement and extension of the 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.

### Details

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
    scalechk   Check scale of initial parameters and bounds

### Author(s)

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

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

### References

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

### See Also

optim

[Package optextras version 2016-8.8 Index]