How can I use fmincon() for different input parameters without using for loop? - optimization

Question

I want to run the optimization function fmincon() over thousands of different input parameters. Briefly, the aim of the optimization is to find the optimal consumption and investment strategy that give the highest utility for a given wealth. The basic set up and functions are given as follows:
library(pracma)
library(NlcOptim)
# individual preference parameters
gamma <- 5
beta <- 0.02
Y <- 1
# financial market parameters
r <- 0.02
mu <- 0.06
sigma <- 0.2
lambda <- (mu-r)/sigma
# Merton fraction
w_star <- lambda / (gamma*sigma)
# fix random seed
set.seed(85)
scenarios <- 1000
Z_omega <- array(rnorm(scenarios,0,1), dim=c(scenarios,1)) # Brownian motion vector for E[J(W)]
# J multiple
multiple <- 1000000000
fineness <- 0.01
# define utility function
u <- function(C) {
C^(1-gamma)/(1-gamma)
}
# wealth scenario at t+1 for a given W_t
W.next <- function(W,C,fstar) {
W.tplus1 <- exp(r + fstar*sigma*lambda - 0.5*fstar^2*sigma^2 + fstar*sigma*Z_omega) * (W + Y - C)
return(W.tplus1)
}
J.simulate <- function(W.tplus1) {
floor.number <- floor((round_any(W.tplus1, fineness, f=floor) * 1/fineness)) + 1
ceiling.number <- ceiling((round_any(W.tplus1, fineness, f=ceiling) * 1/fineness)) + 1
x1 <- G_T[floor.number]
x2 <- G_T[ceiling.number]
y1 <- J_WT[floor.number]
y2 <- J_WT[ceiling.number]
# linear interpolation for J
J.tplus1.simulate <- y1 + ((W.tplus1-x1)/(x2-x1) * (y2-y1))
return(J.tplus1.simulate)
}
# define h(C,f|W)
h_t <- function(Cfstar) {
C <- Cfstar[1]
fstar <- Cfstar[2]
# wealth scenario at t+1 for a given W_t
W.tplus1 <- W.next(W,C,fstar)
# compute indirect utility for simulated W_t+1 using already compute J_WT
J.tplus1.simulate <- J.simulate(W.tplus1) # ignore wealth less than 0.001 (it can never be optimal)
# expectation of all J(W_t+1)
J_t_plus_1 <- mean(J.tplus1.simulate, na.rm=TRUE) # ignore NAs
# function h_t
indirect_utility <- log(-(u(C) + exp(-beta) * J_t_plus_1)*multiple)
return(indirect_utility)
}
For the sake of simplicity, I generated 10 wealth levels, W, to be optimized:
# wealth grid at T
G_T <- c(0.001, seq(0.01, 3, by=0.01))
J_1T <- -291331.95
J_WT <- G_T^(1-gamma) * J_1T
# wealth to be optimized
W_optim <- seq(0.01, 0.1, by=0.01)
What I did using the for loop is as follows:
# number of loop
wealth.loop <- length(W_optim)
# result vectors
C_star <- numeric(wealth.loop)
f_star <- numeric(wealth.loop)
J <- numeric(wealth.loop)
# lowerbound is fixed
lowerbound <- c(0.01,0.0001)
# optimize!
for (g in 1:wealth.loop) {
W <- W_optim[g]
x0 <- c((W+Y)*0.05,w_star) # initial input vector
upperbound <- c(W+Y-0.01,1) # upperbound depending on W
optimization <- fmincon(x0=x0, fn=h_t, lb=lowerbound, ub=upperbound, tol=1e-10)
C_star[g] <- optimization$par[1]
f_star[g] <- optimization$par[2]
J[g] <- optimization$value
print(c(g,optimization$par[1],optimization$par[2]))
}
This works well, but it takes hours to optimize over more than hundred of thousands set of different parameters. Hence, I was looking for some smarter ways of doing this, like using apply-related functions. For instance, I tried:
W <- W_optim
# input matrix
x0 <- matrix(0, nrow=length(W), ncol=2)
x0[,1] <- (W+Y)*0.05
x0[,2] <- w_star
# lowerbound the same
lowerbound <- c(0.01,0.0001)
# upperbound matrix
upperbound <- matrix(0, nrow=length(W), ncol=2)
upperbound[,1] <- W+Y-0.01
upperbound[,2] <- 1
# optimize using mapply
mapply(fmincon, x0=x0, fn=h_t, lb=lowerbound, up=upperbound)
But obviously it doesn't work. I'm not sure whether the problem is using matrix as input parameters, not vector, or I'm just using a wrong function. Is there any way to solve this problem with an efficient & smart coding?
I tried to optimize over the different parameters at once using mapply, but apparently it didn't work. Maybe I should have used another apply-related function or I should make a different structure for the input matrix?