Bitmask DP uses a bitmask to represent a subset (e.g., which nodes are visited). A common form is dp[mask][i] = best result for subset `mask` ending at `i` (used in problems like TSP). Typical complexity is exponential, often O(n^2 * 2^n) time and O(n * 2^n) memory.
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 "bitmask-dp-(subset-dp):-what-is-it-and-what-is-a"
function explain() {
// Start from the core idea:
// Bitmask DP uses a bitmask to represent a subset (e.g., which nodes are visited). A common
}
Common pitfalls
Too generic: no concrete trade-offs or examples.
Mixing average-case and worst-case (e.g., complexity).