Interview kitsBlog

Your dream job? Lets Git IT.
Interactive technical interview preparation platform designed for modern developers.

XGitHub

Platform

  • Categories

Resources

  • Blog
  • About the app
  • FAQ
  • Feedback

Legal

  • Privacy Policy
  • Terms of Service

© 2026 LetsGit.IT. All rights reserved.

LetsGit.IT/Categories/Algorithms
Algorithmshard

Bitmask DP (subset DP): what is it and what is a typical complexity?

Tags
#dp#bitmask#subset#tsp#complexity
Back to categoryPractice quiz

Answer

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:

  • Context (tags): dp, bitmask, subset, tsp, complexity
  • 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).
  • Ignoring constraints: memory, concurrency, network/disk costs.

Interview follow-ups

  • When would you choose an alternative and why?
  • What production issues show up and how do you diagnose them?
  • How would you test edge cases?

Related questions

Algorithms
Heap sort: what are its time complexity, space complexity, and stability?
#heapsort#sorting#complexity
Algorithms
Sliding window: what is it and when is it better than nested loops?
#sliding-window#two-pointers#complexity
Algorithms
What does amortized O(1) mean? Explain with dynamic array growth.
#amortized#complexity#dynamic-array
Algorithms
What does Big-O describe?
#big-o#complexity#performance
Algorithms
QuickSort vs MergeSort?
#sorting#quicksort#mergesort
Algorithms
Explain Binary Search.
#search#binary-search#algorithm