Algorithms/go-perfbook
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Add some pithy 'Advice' lines to the big-O summary

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Also mentioned "anything else between quadratic n² and exponential b^n", which is where a lot of NP-hard optimizations are ending up right now (e.g. O(n² (log log n)² and friends)
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Kane York 2018-03-01 21:35:43 -08:00 committed by Damian Gryski
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performance.md
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@ -335,14 +335,22 @@ shave cycles, but this just prevents dumb performance issues that might not
be noticed until much later. be noticed until much later.
The basic classes of complexity are: The basic classes of complexity are:
* O(1): a field access, array or map lookup * O(1): a field access, array or map lookup
* O(log n): binary search Advice: don't worry about it
* O(n): simple loop * O(log n): binary search
* O(n\*m): nested loop Advice: only a problem if it's in a loop
* O(n log n): divide-and-conquer * O(n): simple loop
* combinatoric - look out!! Advice: you're doing this all the time
* O(n log n): divide-and-conquer, sorting
Advice: still fairly fast
* O(n\*m): nested loop / quadratic
Advice: be careful and constrain your set sizes
* Anything else between quadratic and subexponential
Advice: don't run this on a million rows
* O(b ^ n), O(n!): exponential and up
Advice: good luck if you have more than a dozen or two data points
Link: bigocheatsheet.com Link: http://bigocheatsheet.com
Let's say you need to search through of an unsorted set of data. "I should Let's say you need to search through of an unsorted set of data. "I should
use a binary search" you think, knowing that a binary search O(log n) which use a binary search" you think, knowing that a binary search O(log n) which