1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
|
tn <- function(x, fgfun, trace=0, ...) {
## ---------------------------------------------------------
## this routine solves: minimize f(x)
##
## parameters:
##
## ierror <- error code
## ( 0 <-> normal return)
## ( 2 <-> more than maxfun evaluations)
## ( 3 <-> line search failed (may not be serious)
## (-1 <-> error in input parameters)
## x -> initial estimate of the solution;
## fgfun -> function routine: [f,g] <- fgfun(x)
## xstar <- computed solution.
## g <- final value of the gradient
## f <- final value of the objective function
##
## This function sets up the parameters for lmqn. They are:
## maxfun - maximum allowable number of function evaluations
## stepmx - maximum allowable step in the linesearch
## accrcy - accuracy of computed function values
## maxit - maximum number of inner iterations per step
## ---------------------------------------------------------
n <- length(x)
maxit <- 1 + round((n+1)/2)
maxit <- min(50, maxit);
maxfun <- 150*n;
stepmx <- 10;
eps<- .Machine$double.eps
accrcy <- 100*eps;
## result is list of [xstar, f, g, ierror]
result <- lmqn (x, fgfun, maxit, maxfun, stepmx, accrcy, trace, ...)
rm("envjn", envir=globalenv())
result
}
|
