[Haskell-cafe] Re: List of all powers

apfelmus apfelmus at quantentunnel.de
Wed Nov 14 15:37:34 EST 2007


Brent Yorgey wrote:
> Kurt Hutchinson wrote:
> 
>> As part of a solution I'm working on for Project Euler problem 119, I
>> wanted to create an ordered list of all powers of all positive
>> integers (starting with squares).
> 
> The merging can be done much more simply and efficiently (this is code I
> wrote when computing squarefree numbers in a blog
> post<http://byorgey.wordpress.com/2007/09/01/squarefree-numbers-in-haskell/>a
> few months ago):
> 
> -- merge two nondecreasing lists.
> ( # ) :: (Ord a) => [a] -> [a] -> [a]
> [] # ys = ys
> xs # [] = xs
> xs@(x:xt) # ys@(y:yt) | x < y = x : (xt # ys)
>                       | x > y = y : (xs # yt)
>                       | otherwise = x : (xt # yt)
> 
> -- merge an infinite list of lists, assuming that each list
> -- is nondecreasing and the lists occur in order of their first
> -- element.
> mergeAll :: (Ord a) => [[a]] -> [a]
> mergeAll ([] : zs) = mergeAll zs
> mergeAll (xxs@(x:xs) : yys@(y:ys) : zs)
>     | x < y = x : mergeAll (xs : yys : zs)
>     | otherwise = mergeAll ((xxs # yys) : zs)
> 
> 
> Then you can simply define
> 
> powers = 1 : mergeAll powertable
> 
> I wrote some code to sum the first n powers and print the result, and
> compiled (using -O2) first with your method, then with mergeAll.  Summing
> the first 7000 powers took ~8s and ~0.1s respectively, so that's a pretty
> big speedup. Based on seeing how the times scale, I suspect that your code
> is O(n^2) or something of that sort, whereas mergeAll is essentially linear,
> although without scrutinizing your code more closely I'm not exactly sure
> why that would be the case.

In principle, both Kurt's and even your  mergeAll  are O(n^2), but that 
depends on the actual distribution of the numbers in the powertable. In 
both cases, the optimization over a naive implementation is to increase 
the number of rows to be considered only if the next output came from 
the last row. This is ok since of the last row, only the head element 
was considered so far and the non-considered elements all have to be 
bigger than this.

Kurt's code is slower because it takes the minimum over _all_ the 
considered rows at every step. This is unnecessary since only one 
element changed, many comparisons can be "cached". In other words, this 
calls for a heap. Brent's code does not produce a heap, but I it's still 
able to cache lots of comparisons.

Kurt may want to transpose the  powertable  to

   2^2 3^2 4^2 5^2 ..
   2^3 3^3 4^3 5^3 ..
   2^4 3^4 4^4 ..
   ..

instead of the current

   2^2 2^3 2^4 2^5 ..
   3^2 3^3 3^4 3^5 ..
   4^2 4^3 4^4 ..
   ..

since the first elements of the rows of the former grows far steeper 
than the ones of the latter. This means that only few rows are to be 
considered each turn.

However, Brent may not want to transpose the  powertable  since the 
steep increase in every single row (as opposed to across rows) is 
exactly what makes his code fast. During evaluation, his tower of calls 
to  #  will compare something like the following numbers:

   2^5   3^4   4^3   5^2

Thanks the form of the tower, the comparisons of the first elements are 
"cached". It looks like

   mergeAll $ (2^5:(__ # 3^4:__) # 4^3:__) : (5^2:xs) : __

The winner is 5^2 and  mergeAll  will proceeds by expecting the head of 
xs . But this one will first be compared to  2^5 = minimum [2^5,3^4,4^3] 
  that's what I mean with "cached". Similarly, the winner  3^4 = minimum 
[2^5,3^4] is cached, too. In other words, the minimum of every initial 
segment of the numbers considered is "cached". The crucial point now is 
that those initial numbers quickly become very large (2^7 jumps 
exponentially to 2^8 and so on) and the winners are mostly to be found 
at the end of the list. With the caching, this is cheap to check. If 
Brent were to transpose the  powertable , winners are more to the front 
of the list and the caching is useless, most likely rendering his 
implementation inferior to Kurt's one.


Now, back to the heap and O(n*log n). There is no need to use an 
explicit heap data structure, it's possible to arrange the merges to 
form an implicit heap, i.e. using the best form of "caching". Here's an 
explanation of the technique with mergesort

   http://www.mail-archive.com/[email protected]/msg19980.html

(Hm, does gmane gradually forget old messages?). The only problem is to 
make this work on an infinite list. Dave Bayer discovered a great way to 
do this, here's an explanation

   http://thread.gmane.org/gmane.comp.lang.haskell.cafe/26426/focus=26493


Regards,
apfelmus



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