Dynamic programming helps in solving the LCS problem efficiently by avoiding _______ computations through _______ of previously solved subproblems.
- Duplicate, Division
- Overlapping, Recursion
- Redundant, Exploration
- Repetitive, Memorization
Dynamic programming in the context of solving the Longest Common Subsequence (LCS) problem avoids repetitive computations through the memorization of previously solved subproblems. This optimization technique helps in efficiently finding the LCS by storing and reusing intermediate results.
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