How does dynamic programming optimize the time complexity of finding the Longest Palindromic Substring?
- By employing a greedy strategy to always select the locally optimal solution.
- By memoizing intermediate results to avoid redundant computations.
- By relying on a divide and conquer strategy to break the problem into smaller subproblems.
- By using a bottom-up iterative approach to compare all possible substrings.
Dynamic programming optimizes the time complexity of finding the Longest Palindromic Substring by memoizing intermediate results. This memoization technique helps avoid redundant computations by storing and reusing solutions to subproblems, significantly improving the overall efficiency of the algorithm.
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