recursion vs iteration (was Re: reduce()--what is it good for?)
Bengt Richter
bokr at oz.net
Mon Nov 10 07:06:30 CET 2003
On Sun, 09 Nov 2003 20:56:39 -0800, David Eppstein <eppstein at ics.uci.edu> wrote:
>In article <uq6dnflq6t88lTKiRVn-vw at comcast.com>,
> "Terry Reedy" <tjreedy at udel.edu> wrote:
>
>> "David Eppstein" <eppstein at ics.uci.edu> wrote in message
>> news:eppstein-3EA31B.11393809112003 at news.service.uci.edu...
>> > Recursive memoization can be better than iteration when the
>> > recursion can avoid evaluating a large fraction of the possible
>> > subproblems.
>> >
>> > An example would be the 0-1 knapsack code in
>> > http://www.ics.uci.edu/~eppstein/161/python/knapsack.py
>> >
>> > If there are n items and size limit L, the iterative versions
>> > (pack4 and pack5) take time O(nL), while the recursive version
>> > (pack3) takes time O(min(2^n, nL)). So the recursion can be better
>> > when L is really large.
>>
>> Are you claiming that one *cannot* write an iterative version (with
>> auxiliary stacks) of the same algorithm (which evaluates once each the
>> same restricted subset subproblems) -- or merely that it would be
>> more difficult, and more difficult to recognize correctness (without
>> having mechanically translated the recursive version)?
>
>Of course you can make it iterative with auxiliary stacks.
>Any compiler for a compiled language would do that.
>I don't think of that as removing the recursion, just hiding it.
>
>I thought your question wasn't about semantic games, I thought it was
>about the relation between memoization and dynamic programming. Both
>compute and store the same subproblem values, but in different orders;
>usually they are the same in complexity but not always.
>
>> Standard example: the fibonacci function has at least two
>> non-constant-time, non-memoized algorithms: one exponential (due to
>> gross redundancy) and the other linear.
>
>Not to mention the logarithmic (in number of arithmetic operations)
>algorithms... http://www.cs.utexas.edu/users/EWD/ewd06xx/EWD654.PDF
>
>> Too often, people present recursive exponential and iterative linear
>> algorithms and falsely claim 'iteration is better (faster) than
>> recursion'.
>
>That sounds dishonest. But Fibonacci can be a good example of both
>memoization and dynamic programming, and I would expect the iterative
>linear version to be faster (by a constant factor) than the memoized
>recursive one (also linear due to the memoization).
>
I played with fibonacci and was quite proud of myself for having
probably rediscovered a fast recursion algorithm for it
(about this time 1996 it seems, sheesh time flies) :
in scheme
http://groups.google.com/groups?selm=52s6qm%24rdp%40kanga.accessone.com&output=gplain
and later I translated it to python
http://groups.google.com/groups?selm=3b4a468d.1494788532%40wa.news.verio.net&output=gplain
Perhaps you know of a similar predecessor?
BTW, do you mean dynamic programming above as in Bellman? Recursion can be used in
implementing that, but for fibonacci how would it apply?
Regards,
Bengt Richter
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