[plt-scheme] more possible planet functions

From: Jacob Matthews (jacobm at cs.uchicago.edu)
Date: Wed Feb 7 20:00:10 EST 2007

There are lots of variations on the searching theme tuned to
particular situations like what you describe (e.g., searches that take
advantage of a heuristic function, searches on graphs with high
branching factors) that may be worth your while to read up on. It
sounds to me like you may want to look at A* search:


and iterative deepening depth-first search:


though I don't have anything more concrete to tell you.


On 2/7/07, Joshua Zucker <joshua.zucker at gmail.com> wrote:
> John and all,
> Could you recommend some good reading on breadth-first searches for
> me, or good references on implementations of them in DrScheme?
> One problem I'm currently working on seems to require more about BFS
> than I know or understand.
> Specifically: the breadth of the full search is WAY too big (factorial
> size in n, with n arbitrarily large ...).
> So, to search for "pretty good" nodes of the tree, my idea is to keep
> the best k at each step and search one more node from there, then keep
> the best k of those, and so on until eventually I've found the best
> items I can.
> But if I allow the breadth to get too big (with the sorting needed to
> keep the best k -- or is there a smarter way to keep the best k
> instead of sorting and then keeping the first k?  See, I'm lazy, and
> sort! is built-in which sounded like a good idea for memory use) ...
> well, anyway, when the breadth gets too big it starts spending 90+% of
> its time on swapping instead of actually using the CPU to evaluate
> more nodes of the search tree.
> More specifically:
> I have a moderately-cheap "rough guess at quality" function.
> I have a tree where I start with some number of possible start-nodes
> (there are O(n!) of them too, for n pretty big, but there are some
> that are clearly a lot better than others so it's easy to
> heuristically pick the good ones here).
> Then each node has O(n) descendants, roughly speaking.
> My goal is to find the "farthest' node in the tree -- that is, the one
> that's the most generations from one of the starting points (so I
> guess it's really a forest, not a tree ... but I can pick just one
> starting point at a time).
> So I pretty quickly get up to several thousand nodes in my search as
> I'm hunting .. then I keep the "best' several thousand (based on the
> rough guess at quality), and go one more node from there, and repeat.
> Eventually the branching factor starts to die down and things go a
> little faster, and ultimately I'm left with just one "deepest" node.
> But I know I'm not finding the optimal spot.  One approach is to work
> on improving my rough guess at quality, but my attempts there have led
> nowhere -- in fact to fancier, slower, and less effective guesses.
> So the other approach is to understand BFS better -- how to keep more
> than just several thousand, say several million, without dying in an
> agony of swap.  Or ways of dividing up the search in other ways ...
> I'm sure I can get O(1) improvements in lots of ways, like the data
> structure I use to store each node (right now a vector of small
> integers, but I bet it uses less memory if it were bytes instead of
> vector, for example?) but I think I can get more improvement from
> doing smarter things than sorting and keeping the first k at each step
> down.  But I'm too ignorant to know what those things ought to be.
> Fortunately, ignorance is curable!
> Thanks,
> --Joshua Zucker
> _________________________________________________
>   For list-related administrative tasks:
>   http://list.cs.brown.edu/mailman/listinfo/plt-scheme

Posted on the users mailing list.