Find Longest Common Sequence Using Dynamic Programming. Algorithm finds the longest common sequence in given sequences. There are following apporaches available to find LCS. Recursion; Memoization; ...

In order to find out the complexity of brute force approach, we need to first know the number of possible different subsequences of a string with length n, i.e., find the number of subsequences with ...

We address in this paper the design and analysis of cost-optimal parallel algorithms for solving the problem of the longest common subsequence. Starting from the standard sequential dynamic ...

Dynamic programming is a powerful tool for solving problems such as the Fibonacci sequence, the longest common subsequence of two strings, and the coin change problem.

Equally important is the development of domain-specific adaptations such as the algorithm presented in [2], which utilises principles of dynamic programming and component reusability to tailor bio ...

This paper proposes an efficient parallel algorithm for an important class of dynamic programming problems that includes Viterbi, Needleman–Wunsch, Smith–Waterman, and Longest Common Subsequence. In ...

Obviously, both time and space complexity of dynamic programming based approaches for a MLCS problem with d sequences of length n are O(n d) (Hsu and Du, 1984).Many methods have been proposed to ...