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The Myers diff algorithm performs well when the differences between the two texts are small, because most simple implementations have complexity O((N+M) * D). However when differences are large, it takes a very long time to run. For example, if one of the texts is large and the other is the empty string, many implementations take several minutes to run.
If you knew the differences were large, then you could choose a different algorithm. How do diff tools make this determination in practice?
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I want to align a pair of long texts with ~20M chars each.
I've used in the past Smith-Waterman algorithm but (from my limited understanding) it requires creating a 2-dimensional array with the size of the texts (20M by 20M array) - which is not practical.
So I'm looking for an algorithm to align a pair of long texts that will keep a practical memory size and execution time.
UPDATE
I've also tried Myers and Miller using this implementation: https://www.codeproject.com/Articles/42279/Investigating-Myers-diff-algorithm-Part-of
But I still got out of memory exception on "not so large" texts (1MB).
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I'm researching on compression algorithms (huffman coding and LZ77) and was wondering how I would evaluate their efficiency depending on the input image. I know how they work but I can't find information on their evaluation (mathematically). Thanks!
General-purpose (universal) compressors like LZ77 are usually compared by testing them against a standard set of sources and comparing the results, see: http://www.maximumcompression.com/, http://www.maximumcompression.com/data/summary_mf.php, for example.
Compressors for specific purposes are tested against source sets that are chosen to be as representative as possible.
For some applications it is also useful to place mathematical bounds on compression efficiency in terms of the source entropy.
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Recently I came across a online tool, which given a summation calculates its formula. I have tried for many such summations and has given me correct answer.
I was curious as to which algorithm does it use to solve it.
EDIT: It turns out the tool uses wolframaplha api's. But even if you search on wolfram alpha you will get the same result.
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Scheme lists are slow for random access, which is a common operation in many applications such as image processing. Does this make it naturally handicapped for that kind of application?
If performance is a concern, then you should definitely consider using fixed-access-time structures. Fortunately, Scheme has lots of these, too. The "vector" is the simplest one; it's a close match to what most languages call an "array".
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This is a trivial question - but something I always miss in the day-to-day programming.
Is there a gook lookup reference available for the common algorithms that we usually face in our everyday programming - sorting,sequences,graphs.
The emphasis is more on the applicability and pseudocode ,rather than the mathematical proofs(which I find is what books tend to stress on).
The idea is to keep a ready reference,as and when we need to resort to one of these algorithms into our respective development project and languages.
Dictionary of Algorithms and Data Structures
How about this?
List of algorithms#Wikipedia