Dynamic programming is the technique that turns an exponential brute-force recursion into a fast, polynomial solution by never solving the same subproblem twice. In a quant coding round it is the highest-leverage pattern you can recognize: many questions that look impossible at first (“count the ways”, “minimum cost”, “longest such sequence”) collapse into a few lines once you spot the structure. This lesson shows you how to recognize a DP problem, the two ways to implement it (memoization top-down and tabulation bottom-up), and works two of our interview questions end to end, the Longest Common Subsequence (LCS) and counting zero-sum subarrays.
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