Dynamic Programming Questions

Algorithmic technique for solving problems with overlapping subproblems and optimal substructure. Candidates should demonstrate identifying states and transitions, choosing memoization or bottom up tabulation, analyzing time and space complexity, reconstructing solutions from computed tables, and optimizing space or state when possible. Practice includes classic problems such as longest common subsequence, knapsack, coin change, matrix path problems, and partition problems. Interview assessment focuses on problem formulation, correctness proofs, trade offs between recursion and iterative approaches, and clear coding of the solution with edge case handling and complexity justification.