@cache for memoization

we can use []@cache](https://docs.python.org/3/library/functools.html#functools.cache) for memoization in python

MUST SEE: https://leetcode.com/discuss/general-discussion/1561340/has-anyone-used-python-cache-in-an-interview Documentation: https://docs.python.org/3/library/functools.html#functools.cache

Exampl question: https://leetcode.com/problems/maximum-subarray/submissions/

class Solution:
    def maxSubArray(self, nums: List[int]) -> int:
        @cache # memoization
        def maxStartingAt(i):
            return (
                max(nums[i], nums[i] + maxStartingAt(i + 1))
                if i < len(nums) - 1
                else nums[i]
            )

        return max(maxStartingAt(i) for i in range(len(nums)))

⏫ above code its just recursion, only @cache is added before the recursive function declaration.

It will be very benificial in Dynamic Programming when we dont have much time for creating memoization

Another Example for same Problem:

recursion with @cache for memoization

class Solution:
    def maxSubArray(self, nums):
        @cache
        def solve(i, must_pick):
            if i >= len(nums): return 0 if must_pick else -inf
            return max(nums[i] + solve(i+1, True), 0 if must_pick else solve(i+1, False))
        return solve(0, False)

recursion WITHOUT @cache will also work but gives TLE

class Solution:
    def maxSubArray(self, nums):
        def solve(i, must_pick):
            if i >= len(nums): return 0 if must_pick else -inf
            return max(nums[i] + solve(i+1, True), 0 if must_pick else solve(i+1, False))
        return solve(0, False)