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What Is Sorting Algorithm?
Sorting Algorithms
Sorting algorithms arrange elements in order (ascending, descending, or by a key). The most practical algorithms — quicksort and merge sort — run in O(n log n). Understanding sorting helps with interview prep, but in practice, you'll use built-in sort functions that are already optimized.
How Sorting Algorithm Works
Built-in sorts (Array.sort, Python's sorted) use Timsort — O(n log n), stable, and optimized for real-world data. For interviews, understand: bubble sort (O(n²), simple), merge sort (O(n log n), stable, divide-and-conquer), and quicksort (O(n log n) average, in-place).
Key Concepts
- O(n log n) — The theoretical lower bound for comparison-based sorting — merge sort, quicksort, heapsort achieve this
- Stable Sort — Equal elements maintain their original relative order — merge sort and Timsort are stable
- In-Place — Sorts without extra memory — quicksort is in-place; merge sort requires O(n) extra space
- Quicksort — Average O(n log n), in-place — the most commonly used sorting algorithm in practice
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Frequently Asked Questions
Which sorting algorithm should I learn?
Merge sort (stable, always O(n log n), great for learning divide-and-conquer) and quicksort (fast in practice, commonly used). Understand bubble sort to know why it's bad.
Do I ever implement sorting?
Almost never. Use your language's built-in sort. But understanding sorting algorithms teaches fundamental CS concepts — divide and conquer, recursion, time/space trade-offs.
Want a structured learning path?
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