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    <p>The "hacky" way is the only way to do it. However, you do not need to check i &lt;= numel(x). The array will be expanded automatically (but without array doubling):</p> <pre><code>x = zeros(1000,1); for i = 1:10000 x(i) = rand; end x(i+1:end) = []; </code></pre> <p>EDIT: To keep it simple while still retaining the array doubling, you can write a class, or simply a few helper functions (below).</p> <p>EDIT2: The usage of helper functions will slow things down compared to the manual hack. In MATLAB 2010 it is still much faster than naive growth. In MATLAB 2011 the naive approach is actually faster, suggesting that this version has smarter allocation. Perhaps it is fast enough so that no hack is needed at all. Thanks to Andrew Janke for pointing this out.</p> <pre><code>function listtest() n = 10000; l = new_list(); for i=1:n l = list_append(l, i); end a = list_to_array(l); end function l = new_list() l = [0 0]; end function l = list_append(l, e) if l(1)+1 == length(l) l(length(l)*2) = 0; end l(1) = l(1)+1; l(l(1)+1) = e; end function a = list_to_array(l) a = l(2:1+l(1)); end </code></pre> <p>EDIT (from AndrewJanke)</p> <p>Here's code to compare the speed of the implementations.</p> <pre><code>function manual_reserve_example(n) x = zeros(1000,1); for i = 1:n if i &gt; numel(x) x = [x;zeros(size(x))]; end x(i) = i; end x(i+1:end) = []; end function naive_growth(n) x = 0; for i = 1:n x(i) = i; end end function compare_them(n) fprintf('Doing %d elements in Matlab R%s\n', n, version('-release')); tic; naive_growth(n); fprintf('%30s %.6f sec\n', 'naive_growth', toc); tic; manual_reserve_example(n); fprintf('%30s %.6f sec\n', 'manual_reserve', toc); tic; listtest(n); fprintf('%30s %.6f sec\n', 'listtest', toc); end </code></pre>
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    1. POPre-allocating memory in MATLAB à la std::vector::reserve(n)
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    1. COThe check in the original question implemented an algorithm to double the allocation size on overflow, resulting in `O(log(n))` reallocations (4 in this case). Matlab's natural growth only grows one element at a time, for 9000 reallocations in this example.
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    2. CO@Pursuit: Exactly. This is what `reserve` does as well.
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    3. COrasmus: -1 Nope. The function call overhead (and probable defeating of the JIT's in-place optimizations) makes this helper function implementation much slower than Jacob's manual expansion, or even the naive non-preallocated `x=0; for i=1:n; x(i)=i; end` code. Did you test this? In Matlab, and other languages for that matter, you need to consider the cost of operations and actually measure the performance of code you think is an optimization. Making a class would be even worse due to higher overhead.
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      1. USAndrew Janke
 

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