Big O Notation Time Complexity Analysis
These flashcards cover Big O notation — the standard language for describing algorithm efficiency in CS courses and technical interviews. Learn to classify time complexities from O(1) to O(n!), recognize common patterns, and analyze code for its growth rate. Essential for LeetCode, FAANG interviews, and every algorithms course.
Interactive Deck
5 CardsWhat is amortized time complexity?
Rank these from fastest to slowest
Master this topic effortlessly.
Study G helps you master any topic effortlessly using proven learning algorithms and smart review timing
Download Study GFrequently Asked Questions
What is the difference between Big O, Big Theta, and Big Omega?
Big O is the worst-case upper bound. Big Omega (Ω) is the best-case lower bound. Big Theta (Θ) is the tight bound (both upper and lower). In interviews, 'Big O' typically means tight bound by convention.
How do I calculate the Big O of a nested loop?
Multiply the complexities of nested loops. Two nested loops each iterating n times = O(n²). Three nested = O(n³). If inner loop depends on outer variable, analyze the sum — often still O(n²).
Why do we drop constants in Big O?
Big O describes asymptotic growth as n → ∞. Constants become irrelevant at large scale — O(2n) and O(100n) both grow linearly, so both are O(n). This simplifies comparison between algorithms.