Introduction to vectorizing in MATLAB - any good tutorials? [closed]

Answer 1

The rule of thumb is that you should use built-in matlab functions that operate on arrays in place of loops whenever possible. For example, it seems to me that the problem you've described can be formulated as a convolution, and then you can use matlab's conv2() or filter() functions to implement it without the loop.

Another general trick is to try to formulate your problem in terms of matrix operations.

You should also prefer trading space for time. Let's say you have an n-dimensional vector v and an m x n matrix M, where every row is also an n-dimensional vector. Let's say you want the Euclidean distances between v and every row of M. In this case you should use repmat() to create a matrix containing m copies of v, and calculate the distances using element-wise array operations without a loop.

Answer 2

There's a little writeup that I did a year ago to explain a trick that I found after having spent 3 years writing Matlab code daily, often spending too much time vectorizing everything.

http://www.gyomalin.com/reasonable_vectorization.html

The main idea is that you can get a long way just by vectorizing your code along one dimension. Some of you might have already discovered that trick, but I think it's worth being called a Matlab design pattern.

Answer 3

Here are a couple of MathWorks tutorials I often link to as references on the subject:

• Code Vectorization Guide
• Techniques for Improving Performance: Vectorizing Loops

And here's one of Loren's blog posts that has a nice walkthrough of code vectorization for a particular sample problem:

• Speeding Up MATLAB Applications

The particular type of problem you gave as a sample, which involves processing submatrices of a given matrix, could be vectorized in different ways depending greatly on what sort of operation you are doing. You may be able to use CONV2 or FILTER2 instead of your nested for loops. There are also a number of functions in the Image Processing Toolbox that handle neighborhood and block processing of matrices, such as NLFILTER and BLOCKPROC. The documentation for these functions should help you figure out how to use them as a way to vectorize your code.