R: An R implementation of an unconstrained nonlinear conjugate...

Rcgminu {optimx}

R Documentation

An R implementation of an unconstrained nonlinear conjugate gradient algorithm with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22 for its main structure. CALL THIS VIA `Rcgmin` AND DO NOT USE DIRECTLY.

Description

The purpose of Rcgminu is to minimize an unconstrained function of many parameters by a nonlinear conjugate gradients method. This code is entirely in R to allow users to explore and understand the method.

This code should be called through Rcgmin which selects Rcgminb or Rcgminu according to the presence of bounds and masks.

Usage

   Rcgminu(par, fn, gr, control = list(), ...)

Arguments

`par`	A numeric vector of starting estimates.
`fn`	A function that returns the value of the objective at the supplied set of parameters `par` using auxiliary data in .... The first argument of `fn` must be `par`.
`gr`	A function that returns the gradient of the objective at the supplied set of parameters `par` using auxiliary data in .... The first argument of `fn` must be `par`. This function returns the gradient as a numeric vector. The use of numerical gradients for Rcgminu is STRONGLY discouraged.
`control`	An optional list of control settings.
`...`	Further arguments to be passed to `fn`.

Details

Functions fn must return a numeric value.

The control argument is a list.

maxit: A limit on the number of iterations (default 500). Note that this is used to compute a quantity maxfeval<-round(sqrt(n+1)*maxit) where n is the number of parameters to be minimized.
trace: Set 0 (default) for no output, >0 for trace output (larger values imply more output).
eps: Tolerance used to calculate numerical gradients. Default is 1.0E-7. See source code for Rcgminu for details of application.
dowarn: = TRUE if we want warnings generated by optimx. Default is TRUE.
Note:: The source code Rcgminu for R is likely to remain a work in progress for some time, so users should watch the console output.

As of 2011-11-21 the following controls have been REMOVED

usenumDeriv: There is now a choice of numerical gradient routines. See argument gr.
maximize: To maximize user_function, supply a function that computes (-1)*user_function. An alternative is to call Rcgmin via the package optimx.

Value

A list with components:

`par`	The best set of parameters found.
`value`	The value of the objective at the best set of parameters found.
`counts`	A two-element integer vector giving the number of calls to 'fn' and 'gr' respectively. This excludes those calls needed to compute the Hessian, if requested, and any calls to 'fn' to compute a finite-difference approximation to the gradient.
`convergence`	An integer code. '0' indicates successful convergence. '1' indicates that the function evaluation count 'maxfeval' was reached. '2' indicates initial point is infeasible.
`message`	A character string giving any additional information returned by the optimizer, or 'NULL'.
`bdmsk`	Returned index describing the status of bounds and masks at the proposed solution. Parameters for which bdmsk are 1 are unconstrained or "free", those with bdmsk 0 are masked i.e., fixed. For historical reasons, we indicate a parameter is at a lower bound using -3 or upper bound using -1.

References

See Rcgmin documentation.

An R implementation of an unconstrained nonlinear conjugate gradient algorithm with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22 for its main structure. CALL THIS VIA Rcgmin AND DO NOT USE DIRECTLY.