# Memoization

### From HaskellWiki

**Memoization** is a technique for storing values of a function instead of recomputing them each time the function is called.

## Contents |

## 1 Memoization without recursion

You can just write a memoization function using a data structure that is suitable for your application. We don't go into the details of this case. If you want a general solution for several types,

you need a type class, saymemoize :: Memoizable a => (a->b) -> (a->b)

Now, how to implement something like this? Of course, one needs a finite

map that stores valuesMap () b := b Map (Either a a') b := (Map a b, Map a' b) Map (a,a') b := Map a (Map a' b)

Its construction is based on the following laws for functions

() -> b =~= b (a + a') -> b =~= (a -> b) x (a' -> b) -- = case analysis (a x a') -> b =~= a -> (a' -> b) -- = currying

For further and detailed explanations, see

- R. Hinze: Memo functions, polytypically!
- R. Hinze: Generalizing generalized tries

## 2 Memoization with recursion

Things become more complicated if the function is recursively defined and it shall used memoized calls to itself. A classic example is the recursive computation of Fibonacci numbers.

The naive implementation of Fibonacci numbers without memoization is horribly slow.

Tryslow_fib :: Int -> Integer slow_fib 0 = 0 slow_fib 1 = 1 slow_fib n = slow_fib (n-2) + slow_fib (n-1)

The memoized version is much faster.

Trymemoized_fib :: Int -> Integer memoized_fib = let fib 0 = 0 fib 1 = 1 fib n = memoized_fib (n-2) + memoized_fib (n-1) in (map fib [0 ..] !!)

### 2.1 Memoizing fix point operator

You can factor out the memoizing trick to a function, the memoizing fix point operator,

which we will callfib :: (Int -> Integer) -> Int -> Integer fib f 0 = 1 fib f 1 = 1 fib f n = f (n-1) + f (n-2) fibonacci :: Int -> Integer fibonacci = memoFix fib

I suppose if you want to "put it in a library",

you should just putThis allows the user e.g. to define the data structure used for memoization.

The memoising fixpoint operator works by putting the result of the first call of the function for each natural number into a data structure and using that value for subsequent calls ;-)

In general it is

memoFix :: ((a -> b) -> (a -> b)) -> a -> b memoFix f = let mf = memoize (f mf) in mf

## 3 Efficient tree data structure for maps from Int to somewhere

Here we present a special tree data type which is useful as memoizing data structure e.g. for the Fibonacci function.

memoizeInt :: (Int -> a) -> (Int -> a) memoizeInt f = (fmap f (naturals 1 0) !!!)

A data structure with a node corresponding to each natural number to use as a memo.

data NaturalTree a = Node a (NaturalTree a) (NaturalTree a)

Map the nodes to the naturals in this order:

```
```

```
``` 0
1 2
3 5 4 6
7 ...

```
```

Look up the node for a particular number

Node a tl tr !!! 0 = a Node a tl tr !!! n = if odd n then tl !!! top else tr !!! (top-1) where top = n `div` 2

We surely want to be able to map on these things...

instance Functor NaturalTree where fmap f (Node a tl tr) = Node (f a) (fmap f tl) (fmap f tr)

If only so that we can write cute, but inefficient things like the below,

which is just anaturals = Node 0 (fmap ((+1).(*2)) naturals) (fmap ((*2).(+1)) naturals)

The following is probably more efficient (and, having arguments won't hang around at top level, I think)

-- have I put morenaturals r n = Node n ((naturals $! r2) $! (n+r)) ((naturals $! r2) $! (n+r2)) where r2 = 2*r