Dynamic Programming Quant Interview Guide
Dynamic programming quant interview guide for state definitions, recurrences, base cases, memoization, tabulation, examples, and traps.
Candidates solving optimization, counting, and state problems.
Dynamic programming starts with state
A DP solution needs a state that captures all information needed for the remaining decision. If the state is wrong, the recurrence will be wrong.
Write base cases before transitions
Base cases anchor the recurrence. Then define how larger states depend on smaller states and whether you compute top-down or bottom-up.
Concrete example
In a path-counting problem, dp[i] might represent ways to reach position i, with transitions from previous positions and a base case at the start.
Memoization is often enough
Top-down recursion with memoization can be quicker to write and explain. Bottom-up tabulation may be better when memory or order is important.
Common mistakes
Candidates often force DP onto problems that need a greedy choice or hash map. Use DP when overlapping subproblems and optimal substructure are real.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.