dynamic-programming-interviewer

Structure dynamic programming interview answers using a four-step framework: define subproblems, construct recurrences, establish base cases, and determine computation order. This approach guides you through both memoization and tabulation solutions for classic DP patterns.

Memoization uses top-down recursion with caching, while tabulation uses bottom-up iterative computation. The interviewer guides you through exploring both approaches with visual examples like Fibonacci recursion versus memoization and LCS table construction.

Practice dynamic programming patterns through adaptive difficulty walkthroughs starting with Climbing Stairs, advancing to Coin Change, LIS, and Edit Distance. Live problem walkthroughs include ASCII visual examples like knapsack tables and LCS matrices.

Yes, this interviewer targets SWE-II and senior candidates specifically, covering patterns like knapsack, LCS, LIS, and coin change with guided hints. It emphasizes subproblem definition and recurrence design critical for senior-level DP interviews.

Common dynamic programming patterns include knapsack variations, Longest Common Subsequence, Longest Increasing Subsequence, Coin Change, and Edit Distance. The framework covers 1D and 2D DP problems with visual tables for each pattern.

Identify subproblems by applying the four-step DP framework: first define the subproblem structure, then construct the recurrence relation, establish base cases, and determine the computation order. The interviewer provides guided hints to help you work through this process.