gen-briefly-explain-memoization-and-one-common-misconception-abo-0f76

Briefly explain memoization and one common misconception about it.

# Memoization **Memoization** is an optimization technique that stores the results of expensive function calls and returns the cached result when the same inputs occur again, avoiding redundant computation. ## Simple Example ```python cache = {} def fibonacci(n): if n in cache: return cache[n] if n <= 1: return n result = fibonacci(n-1) + fibonacci(n-2) cache[n] = result return result ``` ## Common Misconception **"Memoization always makes code faster"** This is false. Memoization only helps when: - The function is **called repeatedly with the same inputs** - The **cost of caching** (memory + lookup) is less than recomputing If a function is called once with unique inputs each time, memoization adds overhead without benefit. It also consumes memory proportionally to the number of unique input combinations cached, which can be problematic for functions with many possible inputs.