When should I use memoization in programming?

What is memoization and when should I use it?

Memoization is an optimization technique that stores the results of expensive function calls and returns the cached result when the same inputs occur again. You should use it when you have a function that is called frequently with the same arguments, especially if the function is computationally expensive.

Can you give me an example of where memoization would be useful?

Sure! A classic example is the Fibonacci sequence. Calculating Fibonacci numbers recursively can be very slow due to repeated calculations. By using memoization, you can store previously calculated Fibonacci numbers and retrieve them quickly, significantly improving performance.

Are there any downsides to using memoization?

Yes, while memoization can improve performance, it also uses additional memory to store the cached results. If the input space is large, it can lead to high memory usage. It's important to balance the trade-off between time and space complexity.

How do I implement memoization in Python?

In Python, you can use a dictionary to store results or use the built-in functools.lru_cache decorator, which automatically handles caching for you.