longest common subsequence problem

The operation of diff is based on solving the longest common subsequence problem.

In computer science, the Hunt-McIlroy algorithm is a solution to the longest common subsequence problem.

Longest common subsequence problem takes into account sentence level structure similarity naturally and identifies longest co-occurring in sequence n-grams automatically.

Finding the longest string which is equal to a subsequence of two or more strings is known as the longest common subsequence problem.

The vast majority of text comparison software based on the longest common subsequence problem algorithm incorrectly report moved text as unlinked additions and deletions.

Typically longest common subsequence problem algorithms run in quadratic time and as such would be ill suited to comparing spreadsheets with hundreds of thousands of rows.

Note that subsequence is different from a substring (see substring vs. subsequence) and this should not be confused with the longest common subsequence problem.

Conventional text comparison tools based on the longest common subsequence problem algorithm can potentially miss a lot of similarities between original and modified files, if blocks of text are moved around.

He is best known for his 1975 and 1977 work on the longest common subsequence problem: Hirschberg's algorithm for this problem and for the related string edit distance problem solves it efficiently in only linear space.