Explain Big-O notation. What do O(1), O(n), O(log n), O(n log n), O(n²) mean?

Difficulty: 3/10

time complexity, algorithm analysis, performance tradeoffs

Big-O Notation

Big-O notation describes the asymptotic upper-bound growth of an algorithm's resource usage as input size increases. It allows engineers to compare scalability without depending on a particular machine, programming language, or constant execution time.

javascript

1
O(1): constant growth; input size does not affect the number of operations significantly.
2
O(log n): logarithmic growth; the problem size is repeatedly reduced, as in binary search.
3

Explain Big-O notation. What do O(1), O(n), O(log n), O(n log n), O(n²) mean?

Difficulty: 3/10

time complexity, algorithm analysis, performance tradeoffs

Big-O Notation

javascript

1
O(1): constant growth; input size does not affect the number of operations significantly.
2
O(log n): logarithmic growth; the problem size is repeatedly reduced, as in binary search.
3

Explain Big-O notation. What do O(1), O(n), O(log n), O(n log n), O(n²) mean?

Big-O Notation

Explain Big-O notation. What do O(1), O(n), O(log n), O(n log n), O(n²) mean?

Big-O Notation

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