What is memoization and how does it work?

What is memoization and how does it work?

Memoization is an optimization technique used to speed up function calls by storing the results of expensive function calls and returning the cached result when the same inputs occur again.

Can you give me an example of how to implement memoization in Python?

Sure! Here's a simple example using a recursive Fibonacci function:

def memoize(f):
    cache = {}
    def memoized_f(n):
        if n not in cache:
            cache[n] = f(n)
        return cache[n]
    return memoized_f

@memoize
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

What are the benefits of using memoization?

The main benefits of memoization include improved performance by reducing the number of function calls, especially in recursive algorithms, and saving computation time by avoiding repeated calculations.

Are there any downsides to using memoization?

Yes, memoization can increase memory usage due to storing results in cache, and it may not be suitable for functions with a large number of unique inputs.