Matlab equivalent of Numpy broadcasting?

Loops aren't bad in MATLAB anymore thanks to compiler optimizations like just-in-time acceleration (JITA). etc. Most of the time, I've noticed that a solution with loops in current MATLAB versions is much faster than complicated (albeit, cool :D) one-liners.

bsxfun might do the trick but in my experience, it tends to have memory issues as well but less so than repmat.

So the syntax would be:

AA = bsxfun(@minus,A,b) where b is the vector and A is your big matrix

But I urge you to profile the loopy version and then decide! Most probably, due to memory constraints, you might not have a choice :)

The other answers are a bit out of date -- Matlab R2016b appears to have added broadcasting as a standard feature. An example from that blog post that matches the question:

>> A = ones(2) + [1 5]'A =     2     2     6     6

I don't know if this will speed up the code, but subtraction of a scalar from a vector doesn't have memory issues. Since your matrix size is so asymmetrical, the overhead from a for-loop on the short dimension is negligible.

So maybe

matout = matin;for j = 1:size(matin, 1) %3 in this case     matout(j,:) = matin(j,:) - vec_to_subtract(j);end

of course, you could do this in place, but I didn't know if you wanted to preserve the original matrix.