This is really similar to Fibonacci Sequence problem. I understand the DP optimization with Fibonacci function using the for loop, but I'm having hard time to connect to this problem.
The recursion function I want to optimize is:
def cal(n): if n <= 0: return 1 else: return cal(n-25)+cal(n-26)
Something like this may help:
(It's inspired by previous post)
from functools import cache
@cache
def cal(n):
if n <= 0:
return 1
else:
return cal(n-25) + cal(n-26)
print(cal(100))
The idea of a "for loop optimization" is that we calculate cal(0), cal(1), cal(2), ... consecutively. And when we want cal(n), we already have cal(n-25) and cal(n-26) stored in an array.
So, the following solution has linear complexity for non-negative n:
def cal(n):
mem = [1] # cal(0) is 1
for i in range(1, n + 1):
num = 1 if i - 25 < 0 else mem[i - 25]
num += 1 if i - 26 < 0 else mem[i - 26]
mem.append (num)
return mem[-1]
One can further optimize to make all the values cal(1), cal(2), ..., cal(n) globally available after calculating the last of them.
IndexError: list index out of range?
from itertools import lru_cachethen add@lru_cachedecorator before your function. all done ✅from itertools...line 2)@lru_cache, line 3)def cal(n): ..., line 4)print(cal(some_number))