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I'm working on a multivariate cross-entropy minimization model (for more details about it, see this paper, pp. 32-33). It's purpose is to adjust a prior multivariate distribution (in this case, a gaussian normal) with information on marginals coming from real observations.

The code at the end of the post represents my current implementation. The maths should have been correctly reproduced, unless I missed something critical. The real problem I'm struggling to deal with is the performance of the code.

In the first part of the model, cumulative probabilities have to be computed over all the orthants of the distribution density. This process has a time complexity of 2^N, where N is the number of entities included into the dataset. As long as the number of entities is less than 12, everything is fast enough on my PC. With 20 entities, which is my current target, the model needs to run mvncdf over 1048576 combinations of orthants and this takes forever to finish.

I already improved the code replacing for loops with parfor loops. I'm planning to improve it even further by replacing the built-in mvncdf function with a faster implementation (signally, this one) and by creating a micro-cache for mvncdf function calls (when filling bounds_2, the code actually recalculates cumulative probabilities that have been already computed for bounds_1).

I'm not very familiar with cross-entropy minimization models, so maybe there are math tricks I can use to simplify this calculation. Maybe the code can be vectorized even more. Well... any help or suggestion to improve the calculations speed is more than welcome!

clc();

clear();

% DATA

pods = [0.015; 0.02; 0.013; 0.007; 0.054; 0.034; 0.009; 0.065; 0.029; 0.205];

dts = [2.1; 2; 2.2; 2.4; 1.5; 1.8; 2.3; 1.5; 1.8; 0.8];

% Test of time complexity:

% pods = [pods; pods];

% dts = [dts; dts];

n = numel(pods);

c = eye(n);

% G / BOUNDS FOR 1

g1 = combn([0 1],n);

g1_s = size(g1,1);

bounds_1 = zeros(g1_s,1);

parfor i = 1:g1_s

interval = zeros(n,2);

for j = 1:n

if (g1(i,j) == 0)

interval(j,:) = [-Inf dts(j)];

else

interval(j,:) = [dts(j) Inf];

end

end

bounds_1(i) = mvncdf(interval(:,1),interval(:,2),0,c);

end

% G / BOUNDS FOR 2:N

gaux = combn([0 1],n-1);

g2 = cell(n,1);

g2_s = g1_s / 2;

bounds_2 = zeros(n,g2_s);

for k = 1:n

g2_k = [gaux(:,1:(k-1)) ones(size(gaux,1),1) gaux(:,k:end)];

parfor i = 1:g2_s

interval = zeros(n,2);

for j = 1:n

if (g2_k(i,j) == 0)

interval(j,:) = [-Inf dts(j)];

else

interval(j,:) = [dts(j) Inf];

end

end

bounds_2(k,i) = mvncdf(interval(:,1),interval(:,2),0,c);

end

g2{k} = g2_k;

end

% SOLUTION

options = optimset(optimset(@fsolve),'Display','iter','TolFun',1e-08,'TolX',1e-08);

cns = [1; pods];

x0 = zeros(size(pods,1)+1,1);

lm = fsolve(@(x)objective(x,n,g1,bounds_1,g2,bounds_2,cns),x0,options);

stop = 1;

% Objective function of the model.

function lm = objective(x,n,g1,bounds_1,g2,bounds_2,cns)

mu = x(1);

lambda = x(2:end);

lm = zeros(n + 1,1);

for i = 1:numel(bounds_1)

lm(1) = lm(1) + exp(-g1(i,:) * lambda) * bounds_1(i);

end

for i = 1:n

g2_k = g2{i,1};

for j = 1:size(bounds_2,2)

lm(i+1) = lm(i+1) + exp(-g2_k(j,:) * lambda) * bounds_2(i,j);

end

end

lm = (exp(-1-mu) * lm) - cns;

end

% All combinations of elements.

function [m,i] = combn(v,n)

if ((fix(n) ~= n) || (n < 1) || (numel(n) ~= 1))

error('Parameter N must be a scalar positive integer.');

end

if (isempty(v))

m = [];

i = [];

elseif (n == 1)

m = v(:);

i = (1:numel(v)).';

else

i = combn_local(1:numel(v),n);

m = v(i);

end

function y = combn_local(v,n)

if (n > 1)

[y{n:-1:1}] = ndgrid(v);

y = reshape(cat(n+1,y{:}),[],n);

else

y = v(:);

end

end

end

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