Fast algorithm for the constrained longest common subsequence problem
Sebastian Deorowicz
Abstract
Sebastian Deorowicz
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
OpenAlex reports 10 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
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)