Runtime Analysis
Runtime analysis is a method in computer science for evaluating the efficiency of algorithms by estimating how their execution time or memory usage scales with input size, typically expressed using Big O notation. It focuses on theoretical worst-case, average-case, or best-case scenarios to predict performance without running actual code. This analysis is fundamental for comparing algorithms and designing scalable software systems.
Developers should learn runtime analysis to optimize code performance, especially in data-intensive applications like sorting large datasets, searching databases, or processing real-time streams. It helps in selecting the most efficient algorithms during system design, such as choosing O(log n) binary search over O(n) linear search for sorted data, and is critical for interviews and academic studies in algorithms.