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Using Memoization In Python To Speed Up Slow Functions

by Emil SadekMay 23rd, 2021

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Memoization is an optimization technique that speeds up programs by caching the results of previous function calls. This allows subsequent calls to reuse the cached results, avoiding time-consuming recalculation. The functools module included in Python's standard library provides two useful decorators for memoization. These decorators use a least recently used (LRU) cache, which stores items in order of use, discarding the least used items to make room for new items. Python 3 makes it incredibly easy to memorize functions.

Memoization

Memoization is an optimization technique that speeds up programs by the results of previous function calls. This allows subsequent calls to reuse the cached results, avoiding time-consuming recalculation.

Memoization is commonly used in , where problems can be broken down into simpler sub-problems. One such dynamic programming problem is calculating the nth Fibonacci number.

Fibonacci

The are a sequence of integers where each number is the sum of the two preceding numbers, starting with the numbers 0 and 1.

F(0) = 0
F(1) = 1
F(n) = F(n - 1) + F(n - 2)

A function that calculates the nth Fibonacci number is often implemented .

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

The function calls of

fibonacci(4)

can be visualized with a recursion tree.

Notice that the function is called with the same input multiple times. Particularly

fibonacci(2)

is calculated from scratch twice. As the input increases, the running time grows exponentially. This is suboptimal and can be improved significantly using memoization.

Memoization in Python

Python 3 makes it incredibly easy to memorize functions. The module included in Python's standard library provides two useful decorators for memoization:

lru_cache

(new in Python 3.2) and

cache

(new in Python 3.9). These decorators use a cache, which stores items in order of use, discarding the least recently used items to make room for new items.

To avoid costly repeated function calls,

fibonacci

can be wrapped by

lru_cache

, which saves values that have already been calculated. The size limit of

lru_cache

can be specified with

maxsize

, which has a default value of 128.

from functools import lru_cache


@lru_cache(maxsize=64)
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

Now the recursion tree for

fibonacci(4)

does not have any nodes that occur more than twice. The running time now grows linearly, which is much faster than exponential growth.

The

cache

decorator is equivalent to

lru_cache(maxsize=None)

from functools import cache


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

Since it does not need to discard least recently used items,

cache

is both smaller and faster than

lru_cache

with a size limit.

Conclusion

Memoization improves performance by caching the results of function calls and returning the cached result when the function is called with the same input(s) again.

Python 3 makes it easy to implement memoization. Simply import