Vectorizing a parallel FOR loop across multiple dimensions MATLAB

问题

Please correct me if there are somethings unclear in this question. I have two matrices pop, and ben of 3 dimensions. Call these dimensions as c,t,w . I want to repeat the exact same process I describe below for all of the c dimensions, without using a for loop as that is slow. For the discussion below, fix a value of the dimension c, to explain my thinking, later I will give a MWE. So when c is fixed I have a 2D matrix with dimension t,w.

Now I repeat the entire process (coming below!) for all of the w dimension. If the value of u is zero, then I find the next non zero entry in this same t dimension. I save both this entry as well as the corresponding t index. If the value of u is non zero, I simply store this value and the corresponding t index. Call the index as i - note i would be of dimension (c,t,w). The last entry of every u(c,:,w) is guaranteed to be non zero.

Example if the u(c,:,w) vector is [ 3 0 4 2 0 1], then the corresponding i values are [1,3,3,4,6,6]. Now I take these entries and define a new 3d array of dimension (c,t,w) as follows. I take my B array and do the following what is not a correct syntax but to explain you: B(c,t,w)/u(c,i(c,t,w),w). Meaning I take the B values and divide it by the u values corresponding to the non zero indices of u from i that I computed.

For the above example, the denominator would be [3,4,4,2,1,1]. I hope that makes sense!!

QUESTION: To do this, as this process simply repeats for all c, I can do a very fast vectorizable calculation for a single c. But for multiple c I do not know how to avoid the for loop. I don't knw how to do vectorizable calculations across dimensions. Here is what I did, where c_size is the dimension of c.

for c=c_size:-1:1
    uu=squeeze(pop(c,:,:)) ; % EXTRACT A 2D MATRIX FROM pop.
    BB=squeeze(B(c,:,:)) ; % EXTRACT A 2D MATRIX FROM B
    ii = nan(size(uu));  % Start with all nan values
    [dum_row, ~] = find(uu);  % Get row indices of non-zero values
    ii(uu ~= 0) = dum_row;     % Place row indices in locations of non-zero values
    ii = cummin(ii, 1, 'reverse');  % Column-wise cumulative minimum, starting from bottomi
    dum_i = ii+(time_size+1).*repmat(0:(scenario_size-1), time_size+1, 1);  % Create linear index
    ben(c,:,:) = BB(dum_i)./uu(dum_i);
    i(c,:,:) = ii ;
    clear dum_i dum_row uu BB ii
end

The central question is to avoid this for loop.