Memorization
Memorization is a programming optimization technique that stores the results of expensive function calls and returns the cached result when the same inputs occur again, thereby avoiding redundant computations. It is commonly used in dynamic programming and recursive algorithms to improve performance by trading memory for speed. This technique is fundamental in computer science for solving problems with overlapping subproblems, such as Fibonacci sequence calculation or shortest path algorithms.
Developers should learn and use memorization when working with recursive algorithms or dynamic programming problems where the same subproblems are solved repeatedly, as it can drastically reduce time complexity from exponential to polynomial (e.g., O(2^n) to O(n) in Fibonacci). It is particularly valuable in competitive programming, algorithm design, and performance-critical applications like financial modeling or game AI, where efficiency is paramount. However, it should be applied judiciously to avoid excessive memory usage in large-scale problems.