Memoization: Using the Power of Anime and Manga

Memoization is a technique used in dynamic programming to optimize the computation of complex problems by storing previously calculated results. It is like having an anime or manga encyclopedia to quickly refer to and avoid re-calculating, similar to how avid fans can recall intricate details of their favorite shows or books.

Picture this: you are enjoying a long-running anime series with an intricate plot. You're trying to remember a specific detail from an earlier episode, but you don't want to watch all the episodes again to find it. Instead, you open your trusted anime encyclopedia, which contains all the essential information about each episode. You quickly find the detail you were looking for without rewatching everything.

In dynamic programming, memoization works in a similar way. When solving a complex problem, you break it down into subproblems. Each subproblem is solved only once, and its solution is stored in a memoization table. When encountering a subproblem again, you can directly retrieve the solution from the memoization table, avoiding redundant computations.

Here's an example of memoization in Python:

1# Python code to demonstrate memoization
2
3# Memoization dictionary
4dp = {}
5
6def fibonacci(n):
7    if n <= 1:
8        return n
9    if n not in dp:
10        dp[n] = fibonacci(n-1) + fibonacci(n-2)
11    return dp[n]
12
13# Finding the nth Fibonacci number using memoization
14n = 10
15print("The {}th Fibonacci number is:".format(n), fibonacci(n))

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if __name__ == "__main__":

  # Python logic here

  for i in range(1, 101):

    if i % 3 == 0 and i % 5 == 0:

        print("FizzBuzz")

    elif i % 3 == 0:

        print("Fizz")

    elif i % 5 == 0:

        print("Buzz")

    else:

        print(i)

​

  print("Print something")