I am trying to speed-up the following loop. This loop updates a value function v_d given a guess v_ini_exp. All variables are pre-allocated. From running a profiler it seems that the major bottleneck is the line where I need to discard elements of a matrix which are less than 0. In my model, these values are not feasible. But it seems that this is taking almost 65% of the execution time. I was wondering if there is a way to speed up the loop (provided below)
I can think of two ways to try to speed it up:
(1) Speed-up the line that takes over 65% of the execution time (I tried different formulation but the current one is the fastest I could think of)
(2) Try to vectorize at least one the the loop, but this is unclear to me since the variable d in the inner loop is a matrix.
Is there any way to speed up this loop? I can use parfor to decrease the runtime by around 50% but is there another way to speed up this loop before jumping to use parallel toolbox?
for ii = 1:z_grid_num
q_ini_ii = squeeze(q_ini(ii,:,:));
v_ini_exp_ii = squeeze(v_ini_exp(ii,:,:));
choice = q_ini_ii.*b_grid_choice - k_grid_choice;
for jj = 1:k_grid_num
for kk = 1:b_grid_num
d = wealth(ii,jj,kk) + choice;
vec_d = (d + v_ini_exp_ii).*(d>0) + (-1e30).*(d<=0);
[v_d(ii,jj,kk)] = max(vec_d,[],'all');
end
end
end
