# Memoization

(Difference between revisions)

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

## 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, say
Memoizable
.
`memoize :: Memoizable a => (a->b) -> (a->b)`

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

map that stores values
b
for keys of type
a
. It turns out that such a map can be constructed recursively based on the structure of
a
:
```  Map ()            b  := b
Map (Either a a') b  := (Map a b, Map a' b)
Map (a,a')        b  := Map a (Map a' b)```
Here,
Map a b
is the type of a finite map from keys
a
to values
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

## 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.

Try
slow_fib 30
, not too much higher than that and it hangs.
```slow_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.

Try
memoized_fib 10000
.
```memoized_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 call
memoFix
here.
```fib :: (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 put
fib
in, and allow the user to call
memoFix fib
to make a new version when necessary.

This 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 a
NaturalTree
such that
naturals!!!n == n
:
`naturals = 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 more
\$!
s than necessary?
```naturals r n =
Node n
((naturals \$! r2) \$! (n+r))
((naturals \$! r2) \$! (n+r2))
where r2 = 2*r```