tnbc: Truncated Newton function minimization with bounds...

In optimx: Expanded Replacement and Extension of the 'optim' Function

Truncated Newton function minimization with bounds constraints

Description

A bounds-constarined R implementation of a truncated Newton method for minimization of nonlinear functions subject to bounds (box) constraints.

Usage

   tnbc(x, fgfun, lower, upper, trace=0, ...)

Arguments

x	A numeric vector of starting estimates.
fgfun	A function that returns the value of the objective at the supplied set of parameters par using auxiliary data in .... The gradient is returned as attribute "gradient". The first argument of fgfun must be par.
lower	A vector of lower bounds on the parameters.
upper	A vector of upper bounds on the parameters.
trace	Set >0 to cause intermediate output to allow progress to be followed.
...	Further arguments to be passed to fn.

Details

Function fgfun must return a numeric value in list item f and a numeric vector in list item g.

Value

A list with components:

xstar	The best set of parameters found.
f	The value of the objective at the best set of parameters found.
g	The gradient of the objective at the best set of parameters found.
ierror	An integer indicating the situation on termination. 0 indicates that the method believes it has succeeded; 2 that more than maxfun (default 150*n, where there are n parameters); 3 if the line search appears to have failed (which may not be serious); and -1 if there appears to be an error in the input parameters.
nfngr	A number giving a measure of how many conjugate gradient solutions were used during the minimization process.

References

Stephen G. Nash (1984) "Newton-type minimization via the Lanczos method", SIAM J Numerical Analysis, vol. 21, no. 4, pages 770-788.

For Matlab code, see http://www.netlib.org/opt/tn

See Also

optim

Examples

## See tn.Rd

optimx documentation built on Oct. 24, 2023, 5:06 p.m.