Run tests, where possible, on user objective function and (optionally) gradient and hessian
grchk checks a user-provided R function, ffn.
grchk(xpar, ffn, ggr, trace=0, testtol=(.Machine$double.eps)^(1/3), ...)
xpar |
parameters to the user objective and gradient functions ffn and ggr |
ffn |
User-supplied objective function |
ggr |
User-supplied gradient function |
trace |
set >0 to provide output from grchk to the console, 0 otherwise |
testtol |
tolerance for equality tests |
... |
optional arguments passed to the objective function. |
| Package: | grchk |
| Depends: | R (>= 2.6.1) |
| License: | GPL Version 2. |
numDeriv is used to numerically approximate the gradient of function ffn
and compare this to the result of function ggr.
grchk returns a single object gradOK which is true if the differences
between analytic and approximated gradient are small as measured by the tolerance
testtol.
This has attributes "ga" and "gn" for the analytic and numerically approximated gradients.
At the time of preparation, there are no checks for validity of the gradient code in
ggr as in the function fnchk.
John C. Nash
# Would like examples of success and failure. What about "near misses"??
cat("Show how grchk works\n")
require(numDeriv)
# require(optimx)
jones<-function(xx){
x<-xx[1]
y<-xx[2]
ff<-sin(x*x/2 - y*y/4)*cos(2*x-exp(y))
ff<- -ff
}
jonesg <- function(xx) {
x<-xx[1]
y<-xx[2]
gx <- cos(x * x/2 - y * y/4) * ((x + x)/2) * cos(2 * x - exp(y)) -
sin(x * x/2 - y * y/4) * (sin(2 * x - exp(y)) * 2)
gy <- sin(x * x/2 - y * y/4) * (sin(2 * x - exp(y)) * exp(y)) - cos(x *
x/2 - y * y/4) * ((y + y)/4) * cos(2 * x - exp(y))
gg <- - c(gx, gy)
}
jonesg2 <- function(xx) {
gx <- 1
gy <- 2
gg <- - c(gx, gy)
}
xx <- c(1, 2)
gcans <- grchk(xx, jones, jonesg, trace=1, testtol=(.Machine$double.eps)^(1/3))
gcans
gcans2 <- grchk(xx, jones, jonesg2, trace=1, testtol=(.Machine$double.eps)^(1/3))
gcans2Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.