2007Unpublished venueRequires access

Fast algorithm for the constrained longest common subsequence problem

Sebastian Deorowicz

Open publisher page 10 citations

Abstract

The problem of finding the constrained longest common subsequence (CLCS) for the sequences A and B with respect to the sequence P was introduced recently. The best known algorithms for its solving requires time of order of a product of the sequences length. We introduce a novel approach in which time and memory complexities depends on the number of matches between A, B, and P. The time complexity is never worse than the one of the known algorithms, but typically is better. Experiments show that our method is faster than the known ones and requires less memory. 1 Introduction and

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What this paper is about

The problem of finding the constrained longest common subsequence (CLCS) for the sequences A and B with respect to the sequence P was introduced recently. The best known algorithms for its solving requires time of order of a product of the sequences length. We introduce a novel approach in which time and memory complexities depends on the number of matches between A, B, and P. The time complexity is never worse than the one of the known algorithms, but typically is better. Experiments show that our method is faster than the known ones and requires less memory. 1 Introduction and

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OpenAlex reports 10 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The problem of finding the constrained longest common subsequence (CLCS) for the sequences A and B with respect to the sequence P was introduced recently. The best known algorithms for its solving requires time of order of a product of the sequences length. We introduce a novel approach in which time and memory complexities depends on the number of matches between A, B, and P. The time complexity is never worse than the one of the known algorithms, but typically is better. Experiments show that our method is faster than the known ones and requires less memory. 1 Introduction and

Key concepts: Longest common subsequence problem, Longest increasing subsequence, Subsequence, Computer science, Sequence (biology), Algorithm, Time complexity, Product (mathematics)

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