Hello colleague, On Wednesday, May 21, 2014 7:33:01 AM UTC+2, Andreas Noack Jensen wrote: > > Please consider b += Diagonal(c) instead of diagm. Diagonal(c) only stores > the diagonal elements but works like diagm(c) for matrix arithmetic > > Vectorized code is always easier to understand > > > That is a strong statement. I have vectorised MATLAB code with repmat and > meshgrids that is completely unreadable, but would be fairly easy to follow > if written as loops. I really enjoy that I can just write a loop in Julia > without slow execution. >
If you write code in Matlab (or similar) that's using repmat (nowadays bsxfun) and meshgrids (and reshape) you are operating on wrong side of vectorising your code. Most likely (and there might be good reasons for that) your data is not in the 'right' shape, so you reorder more than you calculate. For problems like this explicit loops are often the better way (so straightforward in julia) OR you take some thinking, why the data needs to be reordered for certain operations. The right side of vectorized code is the SIMD thinking which does operations (some times linalg) on Matrix/Vector forms, and that in the best sense leads to one-liners like M += ((A*B) + (d*C) + (E .* M0)) which are easy to read/write/understand. Expressions like this seems to create in julia an incredible ammount of intermediates and temporary allocations, while actually the output array (M) is already allocated and the + and * operations just needed to be executed with the 'right' indexing. Some people tend to solve this by writing explicit loops and create half a page of code for the above one-liner.
