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diff --git a/docs/implementation/primitive/sort.html b/docs/implementation/primitive/sort.html index 8e69a9ea..12160286 100644 --- a/docs/implementation/primitive/sort.html +++ b/docs/implementation/primitive/sort.html @@ -58,3 +58,6 @@ <p>It's cheap and sometimes worthwhile to trim <code><span class='Value'>π¨</span></code> down to the range of <code><span class='Value'>π©</span></code>. After finding the range of <code><span class='Value'>π©</span></code>, binary cut <code><span class='Value'>π¨</span></code> to a smaller list that contains the range. Stop when the middle element fits inside the range, and search each half of <code><span class='Value'>π¨</span></code> for the appropriate endpoint of the range.</p> <p>If <code><span class='Value'>π©</span></code> is small-range, then a lookup table method is possible. Check the length of <code><span class='Value'>π¨</span></code> because if it's too large then this method is slowerβbinary search doesn't have to hit every element! The approach is simply to create a table of the number of elements in <code><span class='Value'>π¨</span></code> with each value, then take a prefix sum. In BQN, <code><span class='Value'>π©</span><span class='Function'>β+</span><span class='Modifier'>`</span><span class='Paren'>(</span><span class='Number'>1</span><span class='Function'>+β</span><span class='Modifier'>Β΄</span><span class='Value'>π©</span><span class='Paren'>)</span><span class='Function'>β/</span><span class='Modifier'>βΌ</span><span class='Value'>π¨</span></code>, assuming a minimum of 0.</p> <p><a href="#partitioning">Partitioning</a> allows one pivot <code><span class='Value'>t</span></code> from <code><span class='Value'>π¨</span></code> to be compared with all of <code><span class='Value'>π©</span></code> at once. Although the comparison <code><span class='Value'>t</span><span class='Function'>β€</span><span class='Value'>π©</span></code> can be vectorized, the overhead of partitioning still makes this method a little slower per-comparison than sequential binary search <em>when</em> <code><span class='Value'>π¨</span></code> <em>fits in L1 cache</em>. For larger <code><span class='Value'>π¨</span></code> (and randomly positioned <code><span class='Value'>π©</span></code>) cache churn is a huge cost and partitioning can be many times faster. It should be performed recursively, switching to sequential binary search when <code><span class='Value'>π¨</span></code> is small enough. Unlike quicksort there is no difficulty in pivot selection: always take it from the middle of <code><span class='Value'>π¨</span></code> as in a normal binary search. However, there is a potential issue with memory. If <code><span class='Value'>π©</span></code> is unbalanced with respect to <code><span class='Value'>π¨</span></code>, then the larger part can be nearly the whole length of <code><span class='Value'>π©</span></code> (if it's all of <code><span class='Value'>π©</span></code> partitioning isn't actually needed and it doesn't need to be saved). This can require close to <code><span class='Number'>2</span><span class='Function'>β</span><span class='Modifier'>βΌ</span><span class='Function'>β </span><span class='Value'>π¨</span></code> saved partitions of length <code><span class='Function'>β </span><span class='Value'>π©</span></code>, while the expected use would be a total length <code><span class='Function'>β </span><span class='Value'>π©</span></code>.</p> +<h4 id="interpolation-search"><a class="header" href="#interpolation-search">Interpolation search?</a></h4> +<p>Binary search is the optimal approach for truly unknown data. However, if the searched array has an approximately uniform distribution, giving an approximately linear relationship between index and value, <a href="https://en.wikipedia.org/wiki/Interpolation_search">interpolation search</a> uses asymptotically fewer comparisons. Because of the high overhead of index computation (a division!!), it could only ever be useful for large <code><span class='Value'>π¨</span></code>, and <code><span class='Value'>π©</span></code> small enough that partitioning isn't viable.</p> +<p>Interpolation is generally held to be counterproductive because of how badly it fails for non-uniform data. But the continuous-domain equivalent, bracketed root-finding, is better studied, with hybrid approaches developed to fix this. The recently published <a href="https://en.wikipedia.org/wiki/ITP_Method">ITP method</a> (2020) gets the advantages of interpolation with the same maximum number of comparisons as binary search, plus a configurable constant (even if set to 0, it can take advantage of the gap between the length and the next power of 2, but 1 is a better setting). And once the search range is small enough that memory access stops being very expensive, it should switch to binary search and avoid the division.</p> |