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The documentation in <a href="http://www.mathworks.co.uk/help/matlab/ref/for.html" rel="nofollow"><code>for()</code></a> states: <blockquote> <pre><code>for index = values ... end </code></pre> where <code>values</code> has one of the following forms: <ul> <li>...</li> <li><code>valArray</code>: creates a column vector index from subsequent columns of array <code>valArray</code> on each iteration. For example, on the first iteration, <code>index = valArray(:,1)</code>. The loop executes for a maximum of <code>n</code> times, where <code>n</code> is the number of columns of valArray, given by <code>numel(valArray, 1, :)</code>. The input <code>valArray</code> can be of any MATLAB data type, including a string, cell array, or struct.</li> </ul> </blockquote> Therefore, I assume there is a significant overhead and the compiler does not check whether <code>1:ksize == klist</code> to exploit the faster implementation. In other words, per Eitan's comment, the JIT applies to the first two types of accepted values. The whole problem is related to the following indexing task (column vs element): <pre><code>tic for m = 1:100000 for k = 1:ksize klist(:,k); end end toc tic for m = 1:100000 for k = 1:ksize klist(k); end end toc Index column: ~2.9 sec Index element: ~0.28 sec </code></pre> You can see how <code>klist(:,k)</code> effectively slows down the faster loop indicating that the issue in <code>for k = klist</code> is related to the column indexing used in this case. For additional details see this <a href="http://www.mathworks.co.uk/matlabcentral/answers/54522-why-is-indexing-vectors-matrices-in-matlab-very-inefficient" rel="nofollow">lengthy discussion on (inefficient) indexing</a>.
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