to_objective | R Documentation |
Given a madness
object representing a scalar value, strip out
that value and attach an attribute of its derivative as a gradient.
This is a convenience method that simplifies construction of objective
functions for optimization routines.
to_objective(X)
X |
a |
A scalar numeric with a gradient
attribute of the derivative.
An error will be thrown if the value is not a scalar.
Steven E. Pav shabbychef@gmail.com
# an objective function for matrix factorization with penalty:
fitfun <- function(R,L,Y,nu=-0.1) {
dim(R) <- c(length(R),1)
Rmad <- madness(R)
dim(Rmad) <- c(ncol(L),ncol(Y))
Err <- Y - L %*% Rmad
penalty <- sum(exp(nu * Rmad))
fit <- norm(Err,'f') + penalty
to_objective(fit)
}
set.seed(1234)
L <- array(runif(30*5),dim=c(30,5))
Y <- array(runif(nrow(L)*20),dim=c(nrow(L),20))
R0 <- array(runif(ncol(L)*ncol(Y)),dim=c(ncol(L),ncol(Y)))
obj0 <- fitfun(R0,L,Y)
fooz <- nlm(fitfun, R0, L, Y, iterlim=3)
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