Question

After having learned basic programming in Java, I have found that the most difficult part of transitioning to MatLab for my current algorithm course, is to avoid loops. I know that there are plenty of smart ways to vectorize operations in MatLab, but my mind is so “stuck” in loop-thinking, that I am finding it hard to intuitively see how I may vectorize code. Once I am shown how it can be done, it makes sense to me, but I just don’t see it that easily myself. Currently I have the following code for finding the barycentric weights used in Lagrangian interpolation:

function w = barycentric_weights(x);
% The function is used to find the weights of the
% barycentric formula based on a given grid as input.

n = length(x);
w = zeros(1,n);

% Calculating the weights
for i = 1:n
    prod = 1;
    for j = 1:n
        if i ~= j
            prod = prod*(x(i) - x(j));
        end
    end
    w(i) = prod;
end
w = 1./w;

I am pretty sure there must be a smarter way to do this in MatLab, but I just can’t think of it. If anyone has any tips I will be very grateful :). And the only way I’ll ever learn all the vectorizing tricks in MatLab is to see how they are used in various scenarios such as above.

Answer 1

I can see the appeal of vectorization, but I often ask myself how much time it actually saves when I go back to the code a month later and have to decipher all that repmat gibberish. I think your current code is clean and clear and I wouldn’t mess with it unless performance is really critical. But to answer your question here is my best effort:

function w = barycentric_weights_vectorized(x)
    n = length(x);
    w = 1./prod(eye(n) + repmat(x,n,1) - repmat(x',1,n),1);
end

Hope that helps!

And I am assuming x is a row vector here.