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LetsGit.IT/Categories/Algorithms
Algorithmsmedium

Sliding window: what is it and when is it better than nested loops?

Tags
#sliding-window#two-pointers#complexity
Back to categoryPractice quiz

Answer

Sliding window keeps a moving range [l..r] and updates it in one pass. You expand `r` and move `l` to maintain a condition (e.g., sum <= X, at most K distinct). Many problems become O(n) instead of O(n^2) because each pointer moves forward at most n times.

Advanced answer

Deep dive

Expanding on the short answer — what usually matters in practice:

  • Context (tags): sliding-window, two-pointers, 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 "sliding-window:-what-is-it-and-when-is-it-better"
function explain() {
  // Start from the core idea:
  // Sliding window keeps a moving range [l..r] and updates it in one pass. You expand `r` and 
}

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?

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