Time Complexity
Time complexity is a theoretical computer science concept that describes how the runtime of an algorithm scales with the size of its input, typically expressed using Big O notation (e.g., O(n), O(nĀ²)). It provides an asymptotic upper bound on the growth rate of an algorithm's execution time, ignoring constant factors and lower-order terms. This abstraction helps developers analyze and compare algorithm efficiency independently of hardware or implementation details.
Developers should learn time complexity to design and select efficient algorithms for performance-critical applications, such as sorting large datasets, searching in databases, or optimizing real-time systems. It is essential for technical interviews, code reviews, and when working with scalable systems where poor algorithmic choices can lead to bottlenecks, high resource consumption, or unresponsive software.