AP CSABig-O Complexity
Analyze algorithm efficiency using Big-O notation to describe time and space complexity. Compare O(1) constant time, O(log n) logarithmic, O(n) linear, O(n log n) linearithmic, O(n²) quadratic, and O(2ⁿ) exponential growth rates. Visualize how input size affects runtime, understand best/average/worst case scenarios, and learn to identify complexity by analyzing loops, recursion depth, and data structure operations.