optimx: tnbc – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

tnbc

Truncated Newton function minimization with bounds constraints

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

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

`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`.

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

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.

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 tn.Rd

Expanded Replacement and Extension of the 'optim' Function

v2020-4.2

GPL-2

Authors

John C Nash [aut, cre], Ravi Varadhan [aut], Gabor Grothendieck [ctb]

Initial release

2020-04-02