Dynamic programming solves a problem by solving smaller subproblems and saving their results so you don’t recompute them. Use it when subproblems overlap and the best solution can be built from best sub‑solutions (memoization/top‑down or a bottom‑up table).
Advanced answer
Deep dive
Expanding on the short answer — what usually matters in practice:
Complexity: compare typical operations (average vs worst-case).
Invariants: what must always hold for correctness.
When the choice is wrong: production symptoms (latency, GC, cache misses).
Explain the "why", not just the "what" (intuition + consequences).
Trade-offs: what you gain/lose (time, memory, complexity, risk).
Edge cases: empty inputs, large inputs, invalid inputs, concurrency.
Examples
A tiny example (an explanation template):
// Example: discuss trade-offs for "what-is-dynamic-programming?"
function explain() {
// Start from the core idea:
// A method for solving complex problems by breaking them down into simpler subproblems and s
}
Common pitfalls
Too generic: no concrete trade-offs or examples.
Mixing average-case and worst-case (e.g., complexity).