How does dynamic programming help in solving the LCS problem efficiently?
- Applies a greedy algorithm to select the longest subsequence at each step.
- Implements a brute-force approach to explore all possible subproblems.
- Prioritizes sorting the input arrays before finding the longest common subsequence.
- Utilizes memoization to store and reuse intermediate results, reducing redundant computations.
Dynamic programming efficiently solves the LCS problem by utilizing memoization. It stores and reuses intermediate results, eliminating the need to recalculate overlapping subproblems, resulting in a more optimal solution.
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