DSA pattern
Dynamic Programming
Dynamic programming solves complex problems by breaking them into simpler subproblems, storing results to avoid recomputation. Use memoization (top-down) or tabulation (bottom-up) based on the problem structure.
When to use
5 signals- 01Optimization problems (min/max)
- 02Counting problems (number of ways)
- 03Decision problems (is it possible)
- 04Problems with overlapping subproblems
- 05Sequence problems (LCS, LIS)
In interviews, call out the pattern early. It helps interviewers follow your reasoning and keeps your implementation focused. After solving, explain tradeoffs in plain language: why this approach is efficient, where it can break, and what edge cases you would test first.
Practice problems
5 listed- 01Climbing StairsPractice →
- 02Coin ChangePractice →
- 03Longest Common SubsequencePractice →
- 04House RobberPractice →
- 050/1 KnapsackPractice →
Build a review loop
Treat each pattern as a repeating cycle, not a one-time read. Solve a fresh problem today, revisit the same pattern in 2-3 days, and run one timed attempt at the end of the week. This turns short-term familiarity into reliable recall for live interviews.
When reviewing, re-derive the core steps from scratch before checking a reference solution. If you can explain the state transitions and edge cases clearly, you are much more likely to execute well under interview pressure.