PurelyFunctional.tv Newsletter 355: Tip: memoize to trade space for time
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Clojure Tip 💡
memoize to trade space for speed
In the last issue, part of the bonus of the challenge was to use memoization to speed up the straightforward recursive solution. This is a common use case for memoization.
Take, for example, an implementation of Fibonacci that is a direct translation of the mathematical definition.
(defn fib [n] (cond (= 0 n) 0 (= 1 n) 1 :else (+' (fib (- n 2)) (fib (- n 1)))))
Try to run that function, however, on anything bigger than
n=10 and it
takes much longer than you would think. Why? Because it is calculating
the same numbers over and over.
When you call
(fib 50), that will have to calculate
(+ (fib 8) (fib 9)). But
(fib 9) also has to calculate
There's quite a lot of duplication.
We could change the approach and build up the answer starting at the beginning. That would solve this problem and be very fast. But that's hard to read. You lose the natural translation from the mathematical formula.
Another solution is to find a way to store the answer the first time you calculated it so that the next time you need it, you just look it up. That's where memoization comes from.
Memoization basically means wrapping a function with another function.
That wrapper function will cache the answers and the next time you pass
the same arguments, it will return the cached answer. If you apply it to
fib above, it will run fast again, yet the code still reflects the
The Levenshtein distance in the challenge last week had the same problem as Fibonacci. It has a nice, succinct, recursive definition.
lev is called recursively and things are recalculated many times.
Calling this function on long strings will be slow. The answer is to
memoize it. You use space to store the calculated answers, but save time
since you don't have to re-calculate.
If you're interested in how to implement memoization, I have a podcast
episode about it. Clojure
comes with a function called
memoize and there is
also a more feature-rich, configurable and extensible
Next week, I'll go deeper into memoization and its limitations.
Awesome book 📖
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Book update 📖
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I'm currently working on Chapter 7, which is all about Stratified Design. There is so much to say. It's a hard chapter because it's all about design, which takes years of experience to develop a sense for. If someone has the experience, what I say will seem obvious. If they don't have the experience, it will seem arbitrary. How can I condense the experience into the pages so that the reader can see what I am talking about?
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Clojure Challenge 🤔
Last week's challenge
This week's challenge
A neat algorithm for finding a short path through a graph is A-Star. It's used in a lot of games for the characters to find paths through a map.
One thing that's fun about this algorithm is that it's traditionally formulated in terms of mutable data structures. What would this look like as a functional implementation?
Your task is to implement A-Star in Clojure. You can use mutable state if you want! The Wikipedia page has a good description of the algorithm.
As usual, please reply to this email and let me know what you tried. I'll collect them up and share them in the next issue. If you don't want me to share your submission, let me know.