For example if I want to find the gradient and Hessian with respect to x of:
f = function(x,y,alpha,A,b){
return((1/n)*(y-alpha*x)%*%(y-alpha*x) + (A%*%x-b)%*%(A%*%x-b))
}
CRAN package Deriv can compute symbolic derivatives of R functions.
In the code below I have removed the call to return
in the posted function f
.
Function DDeriv
is a copy&paste from this RPubs post changed to use Deriv
instead of base D
that does not accept functions as its first argument.
library(Deriv)
DDeriv <- function(expr, name, order = 1){
if(order < 1) stop("Order must be >= 1")
if(order == 1) Deriv(expr, name)
else DDeriv(Deriv(expr, name), name, order - 1)
}
f <- function(x,y,alpha,A,b){
(1/n)*(y-alpha*x)%*%(y-alpha*x) + (A%*%x-b)%*%(A%*%x-b)
}
Using function Deriv
directly:
Deriv(f, "x")
#function (x, y, alpha, A, b)
#{
# .e1 <- -alpha
# .e3 <- A %*% x - b
# .e5 <- y - alpha * x
# (.e1 %*% .e5 + .e5 %*% .e1)/n + .e3 %*% A + A %*% .e3
#}
Using function DDeriv
:
DDeriv(f, "x", 1)
#function (x, y, alpha, A, b)
#{
# .e1 <- -alpha
# .e3 <- A %*% x - b
# .e5 <- y - alpha * x
# (.e1 %*% .e5 + .e5 %*% .e1)/n + .e3 %*% A + A %*% .e3
#}
DDeriv(f, "x", 2)
#function (x, y, alpha, A, b)
#{
# .e1 <- -alpha
# 2 * (.e1 %*% .e1/n) + 2 * A %*% A
#}