Package: optextras
Version: 2013-10.28
Date: 2013-10-28
Title: Tools to Support Optimization Methods
Authors@R: c( person(given = c("John", "C."), family = "Nash", role =
c("aut", "cre"), email = "nashjc@uottawa.ca") )
Maintainer: John C. Nash <nashjc@uottawa.ca>
Description: Tools to assist in safely applying user-generated objective and
derivative functions to optimization programs. "Optimization" at the
time of writing refers primarily to function minimization methods with at most
bounds and masks on the parameters.
Tools are provided to check the basic computation of objective functions that
the user provides, along with proposed gradient and Hessian functions,
as well as to wrap such functions to avoid failures when inadmissible parameters
are provided. Checks on bounds and masks (fixed parameters) are available, as
are a check on scaling or optimality conditions. An axial search to seek lower
points on the objective function surface is included. Though numDeriv is
used as the primary tool for numerical derivative approximations, forward,
central and backward gradient approximation codes for convenience.
License: GPL-2
Depends: numDeriv
Author: John C. Nash [aut, cre]
NeedsCompilation: no
Packaged: 2014-12-04 16:23:03 UTC; john
Repository: CRAN
Date/Publication: 2014-12-05 23:51:34

Package: optextras
Version: 2016-8.8
Date: 2016-08-08
Title: Tools to Support Optimization Possibly with Bounds and Masks
Authors@R: c( person(given = c("John", "C"), family = "Nash",
role =c("aut", "cre"), email = "nashjc@uottawa.ca") )
Maintainer: John C Nash <nashjc@uottawa.ca>
Description: Tools to assist in safely applying user generated objective and
derivative function to optimization programs. These are primarily function
minimization methods with at most bounds and masks on the parameters.
Provides a way to check the basic computation of objective functions that
the user provides, along with proposed gradient and Hessian functions,
as well as to wrap such functions to avoid failures when inadmissible parameters
are provided. Check bounds and masks. Check scaling or optimality conditions.
Perform an axial search to seek lower points on the objective function surface.
Includes forward, central and backward gradient approximation codes.
License: GPL-2
Depends: numDeriv
NeedsCompilation: no
Packaged: 2016-08-08 13:48:18 UTC; john
Author: John C Nash [aut, cre]
Repository: CRAN
Date/Publication: 2016-08-08 17:08:22