Euler #4 with bigger domain - haskell

Consider the modified Euler problem #4 -- "Find the maximum palindromic number which is a product of two numbers between 100 and 9999."
rev :: Int -> Int
rev x = rev' x 0
rev' :: Int -> Int -> Int
rev' n r
| n == 0 = r
| otherwise = rev' (n `div` 10) (r * 10 + n `mod` 10)
pali :: Int -> Bool
pali x = x == rev x
main :: IO ()
main = print . maximum $ [ x*y | x <- nums, y <- nums, pali (x*y)]
where
nums = [9999,9998..100]
This Haskell solution using -O2 and ghc 7.4.1 takes about 18
seconds.
The similar C solution takes 0.1 second.
So Haskell is 180 times
slower. What's wrong with my solution? I assume that this type of
problems Haskell solves pretty well.
Appendix - analogue C solution:
#define A 100
#define B 9999
int ispali(int n)
{
int n0=n, k=0;
while (n>0) {
k = 10*k + n%10;
n /= 10;
}
return n0 == k;
}
int main(void)
{
int max = 0;
for (int i=B; i>=A; i--)
for (int j=B; j>=A; j--) {
if (i*j > max && ispali(i*j))
max = i*j; }
printf("%d\n", max);
}

The similar C solution
That is a common misconception.
Lists are not loops!
And using lists to emulate loops has performance implications unless the compiler is able to eliminate the list from the code.
If you want to compare apples to apples, write the Haskell structure more or less equivalent to a loop, a tail recursive worker (with strict accumulator, though often the compiler is smart enough to figure out the strictness by itself).
Now let's take a more detailed look. For comparison, the C, compiled with gcc -O3, takes ~0.08 seconds here, the original Haskell, compiled with ghc -O2 takes ~20.3 seconds, with ghc -O2 -fllvm ~19.9 seconds. Pretty terrible.
One mistake in the original code is to use div and mod. The C code uses the equivalent of quot and rem, which map to the machine division instructions and are faster than div and mod. For positive arguments, the semantics are the same, so whenever you know that the arguments are always non-negative, never use div and mod.
Changing that, the running time becomes ~15.4 seconds when compiling with the native code generator, and ~2.9 seconds when compiling with the LLVM backend.
The difference is due to the fact that even the machine division operations are quite slow, and LLVM replaces the division/remainder with a multiply-and-shift operation. Doing the same by hand for the native backend (actually, a slightly better replacement taking advantage of the fact that I know the arguments will always be non-negative) brings its time down to ~2.2 seconds.
We're getting closer, but are still a far cry from the C.
That is due to the lists. The code still builds a list of palindromes (and traverses a list of Ints for the two factors).
Since lists cannot contain unboxed elements, that means there is a lot of boxing and unboxing going on in the code, that takes time.
So let us eliminate the lists, and take a look at the result of translating the C to Haskell:
module Main (main) where
a :: Int
a = 100
b :: Int
b = 9999
ispali :: Int -> Bool
ispali n = go n 0
where
go 0 acc = acc == n
go m acc = go (m `quot` 10) (acc * 10 + (m `rem` 10))
maxpal :: Int
maxpal = go 0 b
where
go mx i
| i < a = mx
| otherwise = go (inner mx b) (i-1)
where
inner m j
| j < a = m
| p > m && ispali p = inner p (j-1)
| otherwise = inner m (j-1)
where
p = i*j
main :: IO ()
main = print maxpal
The nested loop is translated to two nested worker functions, we use an accumulator to store the largest palindrome found so far. Compiled with ghc -O2, that runs in ~0.18 seconds, with ghc -O2 -fllvm it runs in ~0.14 seconds (yes, LLVM is better at optimising loops than the native code generator).
Still not quite there, but a factor of about 2 isn't too bad.
Maybe some find the following where the loop is abstracted out more readable, the generated core is for all intents and purposes identical (modulo a switch of argument order), and the performance of course the same:
module Main (main) where
a :: Int
a = 100
b :: Int
b = 9999
ispali :: Int -> Bool
ispali n = go n 0
where
go 0 acc = acc == n
go m acc = go (m `quot` 10) (acc * 10 + (m `rem` 10))
downto :: Int -> Int -> a -> (a -> Int -> a) -> a
downto high low acc fun = go high acc
where
go i acc
| i < low = acc
| otherwise = go (i-1) (fun acc i)
maxpal :: Int
maxpal = downto b a 0 $ \m i ->
downto b a m $ \mx j ->
let p = i*j
in if mx < p && ispali p then p else mx
main :: IO ()
main = print maxpal

#axblount is at least partly right; the following modification makes the program run almost three times as fast as the original:
maxPalindrome = foldl f 0
where f a x | x > a && pali x = x
| otherwise = a
main :: IO ()
main = print . maxPalindrome $ [x * y | x <- nums, y <- nums]
where nums = [9999,9998..100]
That still leaves a factor 60 slowdown, though.

This is more true to what the C code is doing:
maxpali :: [Int] -> Int
maxpali xs = go xs 0
where
go [] m = m
go (x:xs) m = if x > m && pali(x) then go xs x else go xs m
main :: IO()
main = print . maxpali $ [ x*y | x <- nums, y <- nums ]
where nums = [9999,9998..100]
On my box this takes 2 seconds vs .5 for the C version.

Haskell may be storing that entire list [ x*y | x <- nums, y <- nums, pali (x*y)] where as the C solution calculates the maximum on the fly. I'm not sure about this.
Also the C solution will only calculate ispali if the product beats the previous maximum. I would bet Haskell calculates are palindrome products regardless of whether x*y is a possible max.

It seems to me that you are having a branch prediction problem. In the C code, you have two nested loops and as soon as a palindrome is seen in the inner loop, the rest of the inner loop will be skipped very fast.
The way you feed this list of products instead of the nested loops I am not sure that ghc is doing any of this prediction.

Another way to write this is to use two folds, instead of one fold over the flattened list:
-- foldl g0 0 [x*y | x<-[b-1,b-2..a], y<-[b-1,b-2..a], pali(x*y)] (A)
-- foldl g1 0 [x*y | x<-[b-1,b-2..a], y<-[b-1,b-2..a]] (B)
-- foldl g2 0 [ [x*y | y<-[b-1,b-2..a]] | x<-[b-1,b-2..a]] (C)
maxpal b a = foldl f1 0 [b-1,b-2..a] -- (D)
where
f1 m x = foldl f2 m [b-1,b-2..a]
where
f2 m y | p>m && pali p = p
| otherwise = m
where p = x*y
main = print $ maxpal 10000 100
Seems to run much faster than (B) (as in larsmans's answer), too (only 3x - 4x slower then the following loops-based code). Fusing foldl and enumFromThenTo definitions gets us the "functional loops" code (as in DanielFischer's answer),
maxpal_loops b a = f (b-1) 0 -- (E)
where
f x m | x < a = m
| otherwise = g (b-1) m
where
g y m | y < a = f (x-1) m
| p>m && pali p = g (y-1) p
| otherwise = g (y-1) m
where p = x*y
The (C) variant is very suggestive of further algorithmic improvements (that's outside the scope of the original Q of course) that exploit the hidden order in the lists, destroyed by the flattening:
{- foldl g2 0 [ [x*y | y<-[b-1,b-2..a]] | x<-[b-1,b-2..a]] (C)
foldl g2 0 [ [x*y | y<-[x, x-1..a]] | x<-[b-1,b-2..a]] (C1)
foldl g0 0 [ safehead 0 . filter pali $
[x*y | y<-[x, x-1..a]] | x<-[b-1,b-2..a]] (C2)
fst $ until ... (\(m,s)-> (max m .
safehead 0 . filter pali . takeWhile (> m) $
head s, tail s))
(0,[ [x*y | y<-[x, x-1..a]] | x<-[b-1,b-2..a]]) (C3)
safehead 0 $ filter pali $ mergeAllDescending
[ [x*y | y<-[x, x-1..a]] | x<-[b-1,b-2..a]] (C4)
-}
(C3) can stop as soon as the head x*y in a sub-list is smaller than the currently found maximum. It is what short-cutting functional loops code could achieve, but not (C4), which is guaranteed to find the maximal palindromic number first. Plus, for list-based code its algorithmic nature is more visually apparent, IMO.

Related

Alternative way to write a Haskell function of single input and output of this kind

Good morning everyone!
I'm using the following function as a fitting example of a function that needs to have a simple input and output. In this case it's a function that converts a number from decimal to binary form, as a list of digits no less, just because it is convenient later on.
I chose to write it like this, because even though a number goes in and a list comes out, another structure is needed as an intermediate step, that will hold the digits found so far and hold the quotient of the division, as input for the next step of the loop. I will clean up the necessary mess before outputing anything, though, by selecting the part of the structure that I'm interested in, in this case the second one , and not counters or other stuff, that I'm done with. (As I mentioned this is an example only, and it's not unusual in other cases to initialize the until loop with a triplet like (a,b,c), only to pick one of them at the end, as I see fit, with the help of additional function, like pickXof3.)
So there,
dec2Bin :: Int -> [Int]
dec2Bin num = snd $ until
(\(n,l) -> n <=0) -- test
(\(n,l) -> (fst $ division n, (snd $ division n):l)) -- main function
(num,[]) -- initialization
where division a = divMod a 2
I find it very convenient that Haskell, although lacking traditional for/while loops has a function like until, which reminds me very much of Mathematica's NextWhile, that I'm familiar with.
In the past I would write sth even uglier, like two functions, a "helper" one and a "main" one, like so
dec2BinHelper :: (Int,[Int]) -> (Int,[Int])
dec2BinHelper (n,l)
| n <= 0 = (n,l)
| otherwise = dec2BinHelper (fst $ division n, (snd $ division n):l)
where division a = divMod a 2
-- a function with the sole purpose to act as a front-end to the helper function, initializing its call parameters and picking up its output
dec2Bin :: Int -> [Int]
dec2Bin n = snd $ dec2BinHelper (n,[])
which I think is unnecessarily bloated.
Still, while the use of until allows me to define just one function, I get the feeling that it could be done even simpler/easier to read, perhaps in a way more fitting to functional programming. Is that so? How would you write such a function differently, while keeping the input and output at the absolutely essential values?
I strongly prefer your second solution. I'd start a clean-up with two things: use pattern matching, and use where to hide your helper functions. So:
dec2Bin :: Int -> [Int]
dec2Bin n = snd $ dec2BinHelper (n, []) where
dec2BinHelper (n, l)
| n <= 0 = (n, l)
| otherwise = dec2BinHelper (d, m:l)
where (d, m) = divMod n 2
Now, in the base case, you return a tuple; but then immediately call snd on it. Why not fuse the two?
dec2Bin :: Int -> [Int]
dec2Bin n = dec2BinHelper (n, []) where
dec2BinHelper (n, l)
| n <= 0 = l
| otherwise = dec2BinHelper (d, m:l)
where (d, m) = divMod n 2
There's no obvious reason why you should pass these arguments in a tuple, rather than as separate arguments, which is more idiomatic and saves some allocation/deallocation noise besides.
dec2Bin :: Int -> [Int]
dec2Bin n = dec2BinHelper n [] where
dec2BinHelper n l
| n <= 0 = l
| otherwise = dec2BinHelper d (m:l)
where (d, m) = divMod n 2
You can swap the arguments to dec2BinHelper and eta-reduce; that way, you will not be shadowing the definition of n.
dec2Bin :: Int -> [Int]
dec2Bin = dec2BinHelper [] where
dec2BinHelper l n
| n <= 0 = l
| otherwise = dec2BinHelper (m:l) d
where (d, m) = divMod n 2
Since you know that n > 0 in the recursive call, you can use the slightly faster quotRem in place of divMod. You could also consider using bitwise operations like (.&. 1) and shiftR 1; they may be even better, but you should benchmark to know for sure.
dec2Bin :: Int -> [Int]
dec2Bin = dec2BinHelper [] where
dec2BinHelper l n
| n <= 0 = l
| otherwise = dec2BinHelper (r:l) q
where (q, r) = quotRem n 2
When you don't have a descriptive name for your helper function, it's traditional to name it go or loop.
dec2Bin :: Int -> [Int]
dec2Bin = go [] where
go l n
| n <= 0 = l
| otherwise = go (r:l) q
where (q, r) = quotRem n 2
At this point, the two sides of the conditional are short enough that I'd be tempted to put them on their own line, though this is something of an aesthetic choice.
dec2Bin :: Int -> [Int]
dec2Bin = go [] where
go l n = if n <= 0 then l else go (r:l) q
where (q, r) = quotRem n 2
Finally, a comment on the name: the input isn't really in decimal in any meaningful sense. (Indeed, it's much more physically accurate to think of the input as already being in binary!) Perhaps int2Bin or something like that would be more accurate. Or let the type speak for itself, and just call it toBin.
toBin :: Int -> [Int]
toBin = go [] where
go l n = if n <= 0 then l else go (r:l) q
where (q, r) = quotRem n 2
At this point I'd consider this code quite idiomatic.

Haskell Decimal to Binary

I am trying to build a function that converts a Decimal(Int) into a Binary number.
Unfortunately other than in java it is not possible to divide an int by two in haskell.
I am very new to functional programming so the problem could be something trivial.
So far I could not find another solution to this problem but
here is my first try :
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = if (mod n 2 == 0) then
do
0:fromDecimal(n/2)
else
do
1:fromDecimal(n/2)
I got an java implementation here which I did before :
public void fromDecimal(int decimal){
for (int i=0;i<values.length;i++){
if(decimal % 2 = 0)
values[i]=true ;
decimal = decimal/ 2;
else {values[i]= false;
} }
}
Hopefully this is going to help to find a solution!
There are some problems with your solution. First of all, I advise not to use do at all, until you understand what do does. Here we do not need do at all.
Unfortunately other than in java it is not possible to divide an int by two in haskell.
It actually is, but the / operator (which is in fact the (/) function), has type (/) :: Fractional a => a -> a -> a. An Int is not Fractional. You can perform integer division with div :: Integral a => a -> a -> a.
So then the code looks like:
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = if (mod n 2 == 0) then 0:fromDecimal (div n 2) else 1:fromDecimal (div n 2)
But we can definitely make this more elegant. mod n 2 can only result in two outcomes: 0 and 1, and these are exactly the ones that we use at the left side of the (:) operator.
So we do not need to use an if-then-else at all:
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = mod n 2 : fromDecimal (div n 2)
Likely this is still not exactly what you want: here we write the binary value such that the last element, is the most significant one. This function will add a tailing zero, which does not make a semantical difference (due to that order), but it is not elegant either.
We can define an function go that omits this zero, if the given value is not zero, like:
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = go n
where go 0 = []
go k = mod k 2 : go (div k 2)
If we however want to write the most significant bit first (so in the same order as we write decimal numbers), then we have to reverse the outcome. We can do this by making use of an accumulator:
fromDecimal :: Int -> [Int]
fromDecimal 0 = [0]
fromDecimal n = go n []
where go 0 r = r
go k rs = go (div k 2) (mod k 2:rs)
You cannot / integers in Haskell – division is not defined in terms of integral numbers! For integral division use div function, but in your case more suitable would be divMod that comes with mod gratis.
Also, you are going to get reversed output, so you can reverse manually it after that, or use more memory-efficient version with accumulator:
decToBin :: Int -> [Int]
decToBin = go [] where
go acc 0 = acc
go acc n = let (d, m) = n `divMod` 2 in go (m : acc) d
go will give you an empty list for 0. You may add it manually if the list is empty:
decToBin = (\l -> if null l then [0] else l) . go [] where ...
Think through how your algorithm will work. It starts from 2⁰, so it will generate bits backward from how we ordinarily think of them, i.e., least-significant bit first. Your algorithm can represent non-negative binary integers only.
fromDecimal :: Int -> [Int]
fromDecimal d | d < 0 = error "Must be non-negative"
| d == 0 = [0]
| otherwise = reverse (go d)
where go 0 = []
go d = d `rem` 2 : go (d `div` 2)
In Haskell, when we generate a list in reverse, go ahead and do so but then reverse the result at the end. The reason for this is consing up a list (gluing new items at the head with :) has a constant cost and the reverse at the end has a linear cost — but appending with ++ has a quadratic cost.
Common Haskell style is to have a private inner loop named go that the outer function applies when it’s happy with its arguments. The base case is to terminate with the empty list when d reaches zero. Otherwise, we take the current remainder modulo 2 and then proceed with d halved and truncated.
Without the special case for zero, fromDecimal 0 would be the empty list rather than [0].
The binary numbers are usually strings and not really used in calculations.
Strings are also less complicated.
The pattern of binary numbers is like any other. It repeats but at a faster clip.
Only a small set is necessary to generate up to 256 (0-255) binary numbers.
The pattern can systematically be expanded for more.
The starting pattern is 4, 0-3
bd = ["00","01","10","11"]
The function to combine them into larger numbers is
d2b n = head.drop n $ [ d++e++f++g | d <- bd, e <- bd, f <- bd, g <- bd]
d2b 125
"01111101"
If it's not obvious how to expand, then
bd = ["000","001","010","011","100","101","110","111"]
Will give you up to 4096 binary digits (0-4095). All else stays the same.
If it's not obvious, the db2 function uses 4 pairs of binary numbers so 4 of the set. (2^8) - 1 or (2^12) - 1 is how many you get.
By the way, list comprehension are sugar coated do structures.
Generate the above patterns with
[ a++b | a <- ["0","1"], b <- ["0","1"] ]
["00","01","10","11"]
and
[ a++b++c | a <- ["0","1"], b <- ["0","1"], c <- ["0","1"] ]
["000","001","010","011","100","101","110","111"]
More generally, one pattern and one function may serve the purpose
b2 = ["0","1"]
b4 = [ a++b++c++d | a <- b2, b <- b2, c <- b2, d <- b2]
b4
["0000","0001","0010","0011","0100","0101","0110","0111","1000","1001","1010","1011","1100","1101","1110","1111"]
bb n = head.drop n $ [ a++b++c++d | a <- b4, b <- b4, c <- b4, d <- b4]
bb 32768
"1000000000000000"
bb 65535
"1111111111111111"
To calculate binary from decimal directly in Haskell using subtraction
cvtd n (x:xs) | x>n = 0:(cvtd n xs)
| n>x = 1:(cvtd (n-x) xs)
| True = 1:[0|f<-xs]
Use any number of bits you want, for example 10 bits.
cvtd 639 [2^e|e<-[9,8..0]]
[1,0,0,1,1,1,1,1,1,1]
import Data.List
dec2bin x =
reverse $ binstr $ unfoldr ndiv x
where
binstr = map (\x -> "01" !! x)
exch (a,b) = (b,a)
ndiv n =
case n of
0 -> Nothing
_ -> Just $ exch $ divMod n 2

Haskell Space Leak

all.
While trying to solve some programming quiz:
https://www.hackerrank.com/challenges/missing-numbers
, I came across with space leak.
Main function is difference, which implements multi-set difference.
I've found out that List ':' and Triples (,,) kept on heaps
with -hT option profiling. However, only big lists are difference's
two arguments, and it shrinks as difference keeps on tail recursion.
But the memory consumed by lists keeps increasing as program runs.
Triples is ephemeral array structure, used for bookkeeping the count of multiset's each element. But the memory consumed by triples also
keeps increasing, and I cannot find out why.
Though I've browsed similar 'space leak' questions in stackoverflow,
I couldn't grasp the idea. Surely I have much to study.
I appreciate any comments. Thank you.
p.s) executable is compiled with -O2 switch.
$ ./difference -hT < input04.txt
Stack space overflow: current size 8388608 bytes.
$ ghc --version
The Glorious Glasgow Haskell Compilation System, version 7.6.3
.
import Data.List
import Data.Array
-- array (non-zero-count, start-offset, array_data)
array_size=101
myindex :: Int -> Int -> Int
myindex key offset
| key >= offset = key - offset
| otherwise = key - offset + array_size
mylookup x (_,offset,arr) = arr ! idx
where idx = myindex x offset
addOrReplace :: Int -> Int -> (Int, Int, Array Int (Int,Int)) -> (Int, Int, Array Int (Int,Int))
addOrReplace key value (count,offset,arr) = (count', offset, arr // [(idx,(key,value))])
where idx = myindex key offset
(_,prev_value) = arr ! idx
count' = case (prev_value, value) of
(0,0) -> count
(0,_) -> count + 1
(_,0) -> count - 1
otherwise -> count
difference :: (Int,Int,Array Int (Int,Int)) -> [Int] -> [Int] -> [Int]
difference (count,offset,arr) [] []
| count == 0 = []
| otherwise = [ k | x <- [0..array_size-1], let (k,v) = (arr ! x), v /= 0]
difference m (x:xs) y = difference new_m xs y
where (_,v) = mylookup x m
new_m = addOrReplace x (v + 1) m
difference m [] (y:ys) = difference new_m [] ys
where (_,v) = mylookup y m
new_m = if v == 0
then m
else addOrReplace y (v - 1) m
main = do
n <- readLn :: IO Int
pp <- getLine
m <- readLn :: IO Int
qq <- getLine
let p = map (read :: String->Int) . words $ pp
q = map (read :: String->Int) . words $ qq
startArray = (0,head q, array (0,100) [(i,(0,0)) | i <- [0..100]] )
putStrLn . unwords . map show . sort $ difference startArray q p
[EDIT]
I seq'ed value and Array thanks to Carl's advice.
I attach heap diagram.
[original heap profiling]
[]1
[after seq'ing value v]
difference m (x:xs) y = difference new_m xs y
where (_,v) = mylookup x m
new_m = v `seq` addOrReplace x (v + 1) m
[after seq'ing value v and Array]
difference m (x:xs) y = new_m `seq` difference new_m xs y
where (_,v) = mylookup x m
new_m = v `seq` addOrReplace x (v + 1) m
I see three main problems with this code.
First (and not the cause of the memory use, but definitely the cause of generally poor performance) Array is horrible for this use case. O(1) lookups are useless when updates are O(n).
Speaking of, the values being stored in the Array aren't forced while difference is looping over its first input. They are thunks containing pointers to an unevaluated lookup in the previous version of the array. You can ensure that the value is evaluated at the same time the array is updated, in a variety of ways. When difference loops over its second input, it does this accidentally, in fact, by comparing the value against 0.
Third, difference doesn't even force the evaluation of the new arrays being created while traversing its first argument. Nothing requires the old array to be evaluated during that portion of the loop.
Both of those latter issues need to be resolved to fix the space leak. The first issue doesn't cause a space leak, just much higher overheads than needed.

Solve the equation a * b = c, where a, b and c are natural numbers

I have some natural number c. I want to find all pairs of natural numbers a and b, where a < b, such as a * b = c.
I have a solution:
solve c = do solveHelper [1..c] c where
solveHelper xs c = do
x <- xs
(division, modulo ) <- return (c `divMod` x)
True <- return (modulo == 0)
True <- return (x <= division)
return (x, division)
Example:
*Main> solve 10
[(1,10),(2,5)]
Is there a way to accelerate my code, or a better algorithm I should use?
You can do much, much better. The basic idea is this: first, factorize the number; then enumerate the partitions of the factorization. The product of each partition is a solution. There are fast factorization algorithms out there, but even the naive one is quite an improvement on your code; so:
factorize :: Integer -> [Integer]
factorize n
| n < 1 = error "no. =("
| otherwise = go 2 n
where
go p n | p * p > n = [n]
go p n = case quotRem n p of
(q, 0) -> p:go p q
_ -> go (p+1) n
I will use the very nice multiset-comb package to compute partitions of the set of factors. It doesn't support the usual Foldable/Traversable stuff out of the box, so we have to roll our own product operation -- but in fact this can be a bit more efficient than using the product that the standard interface would give us anyway.
import Math.Combinatorics.Multiset
productMS :: Multiset Integer -> Integer
productMS (MS cs) = product [n^p | (n, p) <- cs]
divisors :: Integer -> [(Integer, Integer)]
divisors n =
[ (a, b)
| (aMS, bMS) <- splits (fromList (factorize n))
, let a = productMS aMS; b = productMS bMS
, a <= b
]
For unfair timings, we can compare in ghci:
*Main> :set +s
*Main> length $ solve (product [1..10])
135
(3.55 secs, 2,884,836,952 bytes)
*Main> length $ divisors (product [1..10])
135
(0.00 secs, 4,612,104 bytes)
*Main> length $ solve (product [1..15])
^CInterrupted. [after several minutes, I gave up]
*Main> length $ divisors (product [1..15])
2016
(0.03 secs, 33,823,168 bytes)
Here solve is your solution, divisors is mine. For a fair comparison, we should compile; I used this program:
main = print . last . solve . product $ [1..11]
(And similar with divisors in place of solve.) I compiled with -O2; yours used 1.367s total, mine 0.002s total.
There's one optimization you don't use: you don't have to try every value from 0 to c.
a < b and a * b = c, so a * a < c, meaning you only have to try numbers from 0 to sqrt c. Or, if you don't want to compute the square root of c, you can stop as soon as a * a >= c.
To do so, you can replace [1..c] by (takeWhile (\x -> x * x < c) [1..]).

Slowdown by removing useless code (Project Euler 23)

I'm trying to optimize my old code from Project Euler #23 and noticed some strange slowdown while removing useless comparisons in a function for list merging.
My code:
import Data.List
import Debug.Trace
limit = 28123
-- sum of all integers from 1 to n
summe :: Int -> Int
summe n = div (n*(n+1)) 2
-- all divisors of x excluding itself
divisors :: Int -> [Int]
divisors x = l1 ++ [x `div` z | z <- l1, z*z /= x, z /= 1]
where m = floor $ sqrt $ fromIntegral x
l1 = [y | y <- [1..m] , mod x y == 0]
-- list of all abundant numbers
liste :: [Int]
liste = [x | x <- [12..limit] , x < sum (divisors x)]
-- nested list with sums of abundent numbers
sumliste :: [[Int]]
sumliste = [[x+y | x <- takeWhile (<=y) liste, x + y <= limit] | y <- liste]
-- reduced list
rsl :: [[Int]] -> [Int]
rsl (hl:[]) = hl
rsl (hl:l) = mergelists hl (rsl l)
-- build a sorted union of two sorted lists
mergelists :: [Int] -> [Int] -> [Int]
mergelists [] [] = []
mergelists [] b = b
mergelists a [] = a
mergelists as#(a:at) bs#(b:bt)
-- | a == b = a : mergelists at bt
-- | a > b = b : mergelists as bt
-- | a < b = a : mergelists at bs
| a == b = if a == hl1
then trace "1" l1
else a : l1
| a > b = if b == hl2
then trace "2" l2
else b : l2
| a < b = if a == hl3
then trace "3" l3
else a : l3
where l1 = mergelists at bt
hl1 = if null l1 then a + 1 else head l1
l2 = mergelists as bt
hl2 = head l2
l3 = mergelists at bs
hl3 = head l3
-- build the sum of target numbers by subtracting sum of nontarget numbers from all numbers
main = print $ (summe limit) - (sum $ rsl sumliste)
My problem is the function mergelists. The body of this functions contains some useless if clauses (as can be seen by the missing trace output) and could be refactored to the three commented lines. The problem with this is an increase of execution time from 3.4s to 5.8s what I can't understand.
Why is the shorter code slower?
As Thomas M. DuBuisson suggested, the problem has to do with the lack of strictness. The following code is a slight modification of the code that you have commented out, which uses the $! operator to ensure that the mergelists call is evaluated before forming the list.
mergelists :: [Int] -> [Int] -> [Int]
mergelists [] [] = []
mergelists [] b = b
mergelists a [] = a
mergelists as#(a:at) bs#(b:bt)
| a == b = (a :) $! mergelists at bt
| a > b = (b :) $! mergelists as bt
| a < b = (a :) $! mergelists at bs
The function $! ensures if the result of (_ :) $! mergelists _ _ is evaluated, then mergelists _ _ must be evaluated as well. Thanks to the recursion, this implies that if the result of mergelists is evaluated, then the entire list must be evaluated.
In the slow version,
mergelists as#(a:at) bs#(b:bt)
| a == b = a : mergelists at bt
| a > b = b : mergelists as bt
| a < b = a : mergelists at bs
you can inspect the first element of the result without evaluating the remainder of the list. The call to mergelists in the tail of the list is stored as an unevaluated thunk. This has various implications:
This is good if you only need a small portion of the merged list, or if the inputs are infinitely long.
On the other hand, if the lists aren't that big to begin with and/or you need all the elements eventually, this adds extra overhead due to the presence of the thunk. It also means that the garbage collector doesn't get to free any of the inputs since the thunks will retain references to them.
I don't understand exactly why it's slower for your particular problem though — perhaps someone more experienced can shed some light on this.
I've noticed that, at -O0, the "slow version" is actually the fastest of the three approaches, so I suspect that GHC was able to take advantage of the strictness and produce more optimized code.

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