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Is it better to use the dimensional cat function(s) versus using brackets?

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I've timed the various methods of concatenating vectors and I'd like someone to interpret the results a little bit. Knowing that MATLAB stores data in column-major order, I have a few questions:
1. Why is horizontal concatenation faster than vertical?
2. Why aren't all of the like-concatenation functions (cat(1,...) and horzcat(...) and [...,...]) equally efficient? Using cat along dimension 1 is the fastest horizontal concatenation, however using brackets is the fastest vertical concatenation.
3. If I am concatenating large vectors many times, what is the optimal way for doing so?
% Given:
rowV1 = randi(1000,1,100);
rowV2 = randi(1000,1,100);
colV1 = rowV1';
colV2 = rowV2';
% Perform:
% Get concatenated column vector.
for i = 1:100000
% Test transposed horizontal concatenation of rows.
% Test vertical concatenation of columns.
% Test vertical concatenation of transposed rows.
% Results:
  1 Kommentar
Stephen23 am 16 Feb. 2017
Bearbeitet: Stephen23 am 16 Feb. 2017
Get rid of the conjugate transpose, otherwise you are comparing apples with oranges.

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Jan am 16 Feb. 2017
If you concatenate two column vectors horizontally:
a = rand(5, 1);
b = rand(5, 1);
c = [a, b]
the contiguos blocks of memory are joined, because Matlab stores the data in columnwise order. In opposite to this, the vertical concatenation:
a = rand(1, 5);
b = rand(1, 5);
c = [a; b]
creates the output by copying one element after the otehr from both vectors, which is less efficient.
These effects are much more important than the question if you use [,] or horzcat. As far as I remember, a profiling with -detail builtin revealed in older Matlab versions, that for [,] the function horzcat is called internally, but I could not reproduce this in R2016b.
For measuring run times prefer either timeit or call the operation in a loop inside the tic/toc to increase the accuracy.
If the brackets are some micro seconds faster or slower than the cat functions, this might change with the next Matlab version. Therefore I would not concentrate on this detail, but use the method, which is better to read: The total time to solve a problem includes the time for programming, debugging and maintenance of the code also. "Premature optimization" is a common pitfall and you find some tutorials, if you search for this term in the net.

Weitere Antworten (1)

Stephen23 am 16 Feb. 2017
I think the answer is "the one that make the code easiest to understand".
Why is this the answer? Because:
  1. commands like this are unlikely to be a major bottleneck of your code, and
  2. the JIT engine and internal optimization can change between MATLAB versions, which means that your finely timed difference of one/two/ten percent might not be relevant at all on another computer or on another version of MATLAB.
There is little point in trying to optimize your way into a corner like that. Most of your time is spent writing/reading/debugging code, so if you really want to save time, use the command that makes your code clearest and the least obfuscated.


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