How to optimize multiple arrays in R? - r

Question

I need minimize a funtion based in Hsieh Model, using R language. The main objective is to minimize a distance function that depends on a set of other functions.
obj = function(x1){
s = sf()
h_til = h_tilf()
w_til = w_tilf(x1)
w_r = w_rf()
p_ir = p_irf()
#H_tr = H_trf(x1)
W = Wf(x1)
f1 = matrix(0, i, r)
f2 = matrix(0, i, r)
for (c in 1:i){
for (j in 1:r){
f1[c, j] = ( (W[c, j] - W_t[c, j]) / W_t[c, j] ) ** 2
f2[c, j] = ( (p_ir[c, j] - p_t[c, j]) / p_t[c, j] ) ** 2
}
}
d1 = sum(f1)
d2 = sum(f2)
D = d1 + d2
return(D)
}
Therefore, my algorithm must find three parameters (w, tau_w, tau_h) that minimize this distance function. These three parameters are arrays with i rows and r columns. Given by:
w = runif(i*r, 0, 1)
tau_w = runif(i*r, -1, 1)
tau_h = runif(i*r, -1, 1)
x1 = array( c(tau_w, tau_h, w), dim = c(i, r, 3))
I trying solve this using optimx and Rsolnp libraries.
res = optim(x1, #starting values
obj) #function to optimise
But i get this error:
Error in x1[c, j, 1] : incorrect number of dimensions
This minimization is usually done using the Nelder-Mead algorithm.
I'm beginner in optimization and apreciate any help. My complete code is here.

Answer 1

The dimensions of the array x1 are lost when you do optim(x1, obj). So the error you get is returned by w_tilf(x1) because it involves x1[c,j,1].
Reconstruct the array at the beginning of the obj function:
obj = function(x1){
x1 = array(x1, dim = c(i, r, 3))
s = sf()
......
}
Then opt <- optim(x1, obj) should work now. It will return the solution in the opt$par field as a vector, you will have to do array(opt$par, dim = c(i, r, 3)) to get an array.