问题
Any pointers on how to solve efficiently the following function in Haskell, for large numbers (n > 108)
f(n) = max(n, f(n/2) + f(n/3) + f(n/4))
I've seen examples of memoization in Haskell to solve fibonacci numbers, which involved computing (lazily) all the fibonacci numbers up to the required n. But in this case, for a given n, we only need to compute very few intermediate results.
Thanks
回答1:
We can do this very efficiently by making a structure that we can index in sub-linear time.
But first,
{-# LANGUAGE BangPatterns #-}
import Data.Function (fix)
Let's define f, but make it use 'open recursion' rather than call itself directly.
f :: (Int -> Int) -> Int -> Int
f mf 0 = 0
f mf n = max n $ mf (n `div` 2) +
mf (n `div` 3) +
mf (n `div` 4)
You can get an unmemoized f by using fix f
This will let you test that f does what you mean for small values of f by calling, for example: fix f 123 = 144
We could memoize this by defining:
f_list :: [Int]
f_list = map (f faster_f) [0..]
faster_f :: Int -> Int
faster_f n = f_list !! n
That performs passably well, and replaces what was going to take O(n^3) time with something that memoizes the intermediate results.
But it still takes linear time just to index to find the memoized answer for mf. This means that results like:
*Main Data.List> faster_f 123801
248604
are tolerable, but the result doesn't scale much better than that. We can do better!
First, let's define an infinite tree:
data Tree a = Tree (Tree a) a (Tree a)
instance Functor Tree where
fmap f (Tree l m r) = Tree (fmap f l) (f m) (fmap f r)
And then we'll define a way to index into it, so we can find a node with index n in O(log n) time instead:
index :: Tree a -> Int -> a
index (Tree _ m _) 0 = m
index (Tree l _ r) n = case (n - 1) `divMod` 2 of
(q,0) -> index l q
(q,1) -> index r q
... and we may find a tree full of natural numbers to be convenient so we don't have to fiddle around with those indices:
nats :: Tree Int
nats = go 0 1
where
go !n !s = Tree (go l s') n (go r s')
where
l = n + s
r = l + s
s' = s * 2
Since we can index, you can just convert a tree into a list:
toList :: Tree a -> [a]
toList as = map (index as) [0..]
You can check the work so far by verifying that toList nats gives you [0..]
Now,
f_tree :: Tree Int
f_tree = fmap (f fastest_f) nats
fastest_f :: Int -> Int
fastest_f = index f_tree
works just like with list above, but instead of taking linear time to find each node, can chase it down in logarithmic time.
The result is considerably faster:
*Main> fastest_f 12380192300
67652175206
*Main> fastest_f 12793129379123
120695231674999
In fact it is so much faster that you can go through and replace Int with Integer above and get ridiculously large answers almost instantaneously
*Main> fastest_f' 1230891823091823018203123
93721573993600178112200489
*Main> fastest_f' 12308918230918230182031231231293810923
11097012733777002208302545289166620866358
回答2:
Edward's answer is such a wonderful gem that I've duplicated it and provided implementations of memoList and memoTree combinators that memoize a function in open-recursive form.
{-# LANGUAGE BangPatterns #-}
import Data.Function (fix)
f :: (Integer -> Integer) -> Integer -> Integer
f mf 0 = 0
f mf n = max n $ mf (div n 2) +
mf (div n 3) +
mf (div n 4)
-- Memoizing using a list
-- The memoizing functionality depends on this being in eta reduced form!
memoList :: ((Integer -> Integer) -> Integer -> Integer) -> Integer -> Integer
memoList f = memoList_f
where memoList_f = (memo !!) . fromInteger
memo = map (f memoList_f) [0..]
faster_f :: Integer -> Integer
faster_f = memoList f
-- Memoizing using a tree
data Tree a = Tree (Tree a) a (Tree a)
instance Functor Tree where
fmap f (Tree l m r) = Tree (fmap f l) (f m) (fmap f r)
index :: Tree a -> Integer -> a
index (Tree _ m _) 0 = m
index (Tree l _ r) n = case (n - 1) `divMod` 2 of
(q,0) -> index l q
(q,1) -> index r q
nats :: Tree Integer
nats = go 0 1
where
go !n !s = Tree (go l s') n (go r s')
where
l = n + s
r = l + s
s' = s * 2
toList :: Tree a -> [a]
toList as = map (index as) [0..]
-- The memoizing functionality depends on this being in eta reduced form!
memoTree :: ((Integer -> Integer) -> Integer -> Integer) -> Integer -> Integer
memoTree f = memoTree_f
where memoTree_f = index memo
memo = fmap (f memoTree_f) nats
fastest_f :: Integer -> Integer
fastest_f = memoTree f
回答3:
Not the most efficient way, but does memoize:
f = 0 : [ g n | n <- [1..] ]
where g n = max n $ f!!(n `div` 2) + f!!(n `div` 3) + f!!(n `div` 4)
when requesting f !! 144, it is checked that f !! 143 exists, but its exact value is not calculated. It's still set as some unknown result of a calculation. The only exact values calculated are the ones needed.
So initially, as far as how much has been calculated, the program knows nothing.
f = ....
When we make the request f !! 12, it starts doing some pattern matching:
f = 0 : g 1 : g 2 : g 3 : g 4 : g 5 : g 6 : g 7 : g 8 : g 9 : g 10 : g 11 : g 12 : ...
Now it starts calculating
f !! 12 = g 12 = max 12 $ f!!6 + f!!4 + f!!3
This recursively makes another demand on f, so we calculate
f !! 6 = g 6 = max 6 $ f !! 3 + f !! 2 + f !! 1
f !! 3 = g 3 = max 3 $ f !! 1 + f !! 1 + f !! 0
f !! 1 = g 1 = max 1 $ f !! 0 + f !! 0 + f !! 0
f !! 0 = 0
Now we can trickle back up some
f !! 1 = g 1 = max 1 $ 0 + 0 + 0 = 1
Which means the program now knows:
f = 0 : 1 : g 2 : g 3 : g 4 : g 5 : g 6 : g 7 : g 8 : g 9 : g 10 : g 11 : g 12 : ...
Continuing to trickle up:
f !! 3 = g 3 = max 3 $ 1 + 1 + 0 = 3
Which means the program now knows:
f = 0 : 1 : g 2 : 3 : g 4 : g 5 : g 6 : g 7 : g 8 : g 9 : g 10 : g 11 : g 12 : ...
Now we continue with our calculation of f!!6:
f !! 6 = g 6 = max 6 $ 3 + f !! 2 + 1
f !! 2 = g 2 = max 2 $ f !! 1 + f !! 0 + f !! 0 = max 2 $ 1 + 0 + 0 = 2
f !! 6 = g 6 = max 6 $ 3 + 2 + 1 = 6
Which means the program now knows:
f = 0 : 1 : 2 : 3 : g 4 : g 5 : 6 : g 7 : g 8 : g 9 : g 10 : g 11 : g 12 : ...
Now we continue with our calculation of f!!12:
f !! 12 = g 12 = max 12 $ 6 + f!!4 + 3
f !! 4 = g 4 = max 4 $ f !! 2 + f !! 1 + f !! 1 = max 4 $ 2 + 1 + 1 = 4
f !! 12 = g 12 = max 12 $ 6 + 4 + 3 = 13
Which means the program now knows:
f = 0 : 1 : 2 : 3 : 4 : g 5 : 6 : g 7 : g 8 : g 9 : g 10 : g 11 : 13 : ...
So the calculation is done fairly lazily. The program knows that some value for f !! 8 exists, that it's equal to g 8, but it has no idea what g 8 is.
回答4:
This is an addendum to Edward Kmett's excellent answer.
When I tried his code, the definitions of nats and index seemed pretty mysterious, so I write an alternative version that I found easier to understand.
I define index and nats in terms of index' and nats'.
index' t n is defined over the range [1..]. (Recall that index t is defined over the range [0..].) It works searches the tree by treating n as a string of bits, and reading through the bits in reverse. If the bit is 1, it takes the right-hand branch. If the bit is 0, it takes the left-hand branch. It stops when it reaches the last bit (which must be a 1).
index' (Tree l m r) 1 = m
index' (Tree l m r) n = case n `divMod` 2 of
(n', 0) -> index' l n'
(n', 1) -> index' r n'
Just as nats is defined for index so that index nats n == n is always true, nats' is defined for index'.
nats' = Tree l 1 r
where
l = fmap (\n -> n*2) nats'
r = fmap (\n -> n*2 + 1) nats'
nats' = Tree l 1 r
Now, nats and index are simply nats' and index' but with the values shifted by 1:
index t n = index' t (n+1)
nats = fmap (\n -> n-1) nats'
回答5:
As stated in Edward Kmett's answer, to speed things up, you need to cache costly computations and be able to access them quickly.
To keep the function non monadic, the solution of building an infinite lazy tree, with an appropriate way to index it (as shown in previous posts) fulfills that goal. If you give up the non-monadic nature of the function, you can use the standard associative containers available in Haskell in combination with “state-like” monads (like State or ST).
While the main drawback is that you get a non-monadic function, you do not have to index the structure yourself anymore, and can just use standard implementations of associative containers.
To do so, you first need to re-write you function to accept any kind of monad:
fm :: (Integral a, Monad m) => (a -> m a) -> a -> m a
fm _ 0 = return 0
fm recf n = do
recs <- mapM recf $ div n <$> [2, 3, 4]
return $ max n (sum recs)
For your tests, you can still define a function that does no memoization using Data.Function.fix, although it is a bit more verbose:
noMemoF :: (Integral n) => n -> n
noMemoF = runIdentity . fix fm
You can then use State monad in combination with Data.Map to speed things up:
import qualified Data.Map.Strict as MS
withMemoStMap :: (Integral n) => n -> n
withMemoStMap n = evalState (fm recF n) MS.empty
where
recF i = do
v <- MS.lookup i <$> get
case v of
Just v' -> return v'
Nothing -> do
v' <- fm recF i
modify $ MS.insert i v'
return v'
With minor changes, you can adapt the code to works with Data.HashMap instead:
import qualified Data.HashMap.Strict as HMS
withMemoStHMap :: (Integral n, Hashable n) => n -> n
withMemoStHMap n = evalState (fm recF n) HMS.empty
where
recF i = do
v <- HMS.lookup i <$> get
case v of
Just v' -> return v'
Nothing -> do
v' <- fm recF i
modify $ HMS.insert i v'
return v'
Instead of persistent data structures, you may also try mutable data structures (like the Data.HashTable) in combination with the ST monad:
import qualified Data.HashTable.ST.Linear as MHM
withMemoMutMap :: (Integral n, Hashable n) => n -> n
withMemoMutMap n = runST $
do ht <- MHM.new
recF ht n
where
recF ht i = do
k <- MHM.lookup ht i
case k of
Just k' -> return k'
Nothing -> do
k' <- fm (recF ht) i
MHM.insert ht i k'
return k'
Compared to the implementation without any memoization, any of these implementation allows you, for huge inputs, to get results in micro-seconds instead of having to wait several seconds.
Using Criterion as benchmark, I could observe that the implementation with the Data.HashMap actually performed slightly better (around 20%) than that the Data.Map and Data.HashTable for which the timings were very similar.
I found the results of the benchmark a bit surprising. My initial feeling was that the HashTable would outperform the HashMap implementation because it is mutable. There might be some performance defect hidden in this last implementation.
回答6:
A couple years later, I looked at this and realized there's a simple way to memoize this in linear time using zipWith and a helper function:
dilate :: Int -> [x] -> [x]
dilate n xs = replicate n =<< xs
dilate has the handy property that dilate n xs !! i == xs !! div i n.
So, supposing we're given f(0), this simplifies the computation to
fs = f0 : zipWith max [1..] (tail $ fs#/2 .+. fs#/3 .+. fs#/4)
where (.+.) = zipWith (+)
infixl 6 .+.
(#/) = flip dilate
infixl 7 #/
Looking a lot like our original problem description, and giving a linear solution (sum $ take n fs will take O(n)).
回答7:
Yet another addendum to Edward Kmett's answer: a self-contained example:
data NatTrie v = NatTrie (NatTrie v) v (NatTrie v)
memo1 arg_to_index index_to_arg f = (\n -> index nats (arg_to_index n))
where nats = go 0 1
go i s = NatTrie (go (i+s) s') (f (index_to_arg i)) (go (i+s') s')
where s' = 2*s
index (NatTrie l v r) i
| i < 0 = f (index_to_arg i)
| i == 0 = v
| otherwise = case (i-1) `divMod` 2 of
(i',0) -> index l i'
(i',1) -> index r i'
memoNat = memo1 id id
Use it as follows to memoize a function with a single integer arg (e.g. fibonacci):
fib = memoNat f
where f 0 = 0
f 1 = 1
f n = fib (n-1) + fib (n-2)
Only values for non-negative arguments will be cached.
To also cache values for negative arguments, use memoInt, defined as follows:
memoInt = memo1 arg_to_index index_to_arg
where arg_to_index n
| n < 0 = -2*n
| otherwise = 2*n + 1
index_to_arg i = case i `divMod` 2 of
(n,0) -> -n
(n,1) -> n
To cache values for functions with two integer arguments use memoIntInt, defined as follows:
memoIntInt f = memoInt (\n -> memoInt (f n))
回答8:
A solution without indexing, and not based on Edward KMETT's.
I factor out common subtrees to a common parent (f(n/4) is shared between f(n/2) and f(n/4), and f(n/6) is shared between f(2) and f(3)). By saving them as a single variable in the parent, the calculation of the subtree is done once.
data Tree a =
Node {datum :: a, child2 :: Tree a, child3 :: Tree a}
f :: Int -> Int
f n = datum root
where root = f' n Nothing Nothing
-- Pass in the arg
-- and this node's lifted children (if any).
f' :: Integral a => a -> Maybe (Tree a) -> Maybe (Tree a)-> a
f' 0 _ _ = leaf
where leaf = Node 0 leaf leaf
f' n m2 m3 = Node d c2 c3
where
d = if n < 12 then n
else max n (d2 + d3 + d4)
[n2,n3,n4,n6] = map (n `div`) [2,3,4,6]
[d2,d3,d4,d6] = map datum [c2,c3,c4,c6]
c2 = case m2 of -- Check for a passed-in subtree before recursing.
Just c2' -> c2'
Nothing -> f' n2 Nothing (Just c6)
c3 = case m3 of
Just c3' -> c3'
Nothing -> f' n3 (Just c6) Nothing
c4 = child2 c2
c6 = f' n6 Nothing Nothing
main =
print (f 123801)
-- Should print 248604.
The code doesn't easily extend to a general memoization function (at least, I wouldn't know how to do it), and you really have to think out how subproblems overlap, but the strategy should work for general multiple non-integer parameters. (I thought it up for two string parameters.)
The memo is discarded after each calculation. (Again, I was thinking about two string parameters.)
I don't know if this is more efficient than the other answers. Each lookup is technically only one or two steps ("Look at your child or your child's child"), but there might be a lot of extra memory use.
Edit: This solution isn't correct yet. The sharing is incomplete.
Edit: It should be sharing subchildren properly now, but I realized that this problem has a lot of nontrivial sharing: n/2/2/2 and n/3/3 might be the same. The problem is not a good fit for my strategy.
来源：https://stackoverflow.com/questions/3208258/memoization-in-haskell