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11.6: Memos

  • Page ID
    40792
  • If you played with the fibonacci function from Section 6.7, you might have noticed that the bigger the argument you provide, the longer the function takes to run. Furthermore, the run time increases quickly.

    To understand why, consider Figure \(\PageIndex{1}\), which shows the call graph for fibonacci with n=4:

    Call graph.
    Figure \(\PageIndex{1}\): Call graph.

    A call graph shows a set of function frames, with lines connecting each frame to the frames of the functions it calls. At the top of the graph, fibonacci with n=4 calls fibonacci with n=3 and n=2. In turn, fibonacci with n=3 calls fibonacci with n=2 and n=1. And so on.

    Count how many times fibonacci(0) and fibonacci(1) are called. This is an inefficient solution to the problem, and it gets worse as the argument gets bigger.

    One solution is to keep track of values that have already been computed by storing them in a dictionary. A previously computed value that is stored for later use is called a memo. Here is a “memoized” version of fibonacci:

    known = {0:0, 1:1}
    
    def fibonacci(n):
        if n in known:
            return known[n]
    
        res = fibonacci(n-1) + fibonacci(n-2)
        known[n] = res
        return res
    

    known is a dictionary that keeps track of the Fibonacci numbers we already know. It starts with two items: 0 maps to 0 and 1 maps to 1.

    Whenever fibonacci is called, it checks known. If the result is already there, it can return immediately. Otherwise it has to compute the new value, add it to the dictionary, and return it.

    If you run this version of fibonacci and compare it with the original, you will find that it is much faster.

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