Skip to main content

Speed Optimization of AMG

Now that AMG's results are getting better, I've started to look at improvements to the speed. At the moment, it's about 10 times as slow as OMG - probably due to my use of an inferior canonical checker (ie: not nauty).

One obvious improvement has been to avoid extending molecules with too many or too few hydrogens. Previously, the hydrogens were only checked for the leaves - that is, at the final step. It is possible to prune the generation tree earlier, if you make two simple assumptions (see image).


The min extension checks the smallest possible way to add all the remaining atoms to the molecule - which is just a tree. This gives the maximum number of hydrogens that could be added at this point. The max extension does the opposite, checking the case where all remaining atoms are added (at their maximum valence) which has the effect of effectively removing hydrogens.

So, the number of implicit hydrogens for a partial structure is added or removed by these two bounds, and the target hydrogen count is checked to see if it lies in this range. The effect is to prune branches of the generation tree before they even need to be checked for canonicity or other expensive tests:


The tree here is arranged so that the number of hydrogens decreases from left to right - although this may not be the order the structures are actually visited. The dashed lines indicate how the side branches are pruned away.

Comments

Popular posts from this blog

Generating Dungeons With BSP Trees or Sliceable Rectangles

So, I admit that the original reason for looking at sliceable rectangles was because of this gaming stackoverflow question about generating dungeon maps. The approach described there uses something called a binary split partition tree (BSP Tree) that's usually used in the context of 3D - notably in the rendering engine of the game Doom. Here is a BSP tree, as an example:



In the image, we have a sliced rectangle on the left, with the final rectangles labelled with letters (A-E) and the slices with numbers (1-4). The corresponding tree is on the right, with the slices as internal nodes labelled with 'h' for horizontal and 'v' for vertical. Naturally, only the leaves correspond to rectangles, and each internal node has two children - it's a binary tree.

So what is the connection between such trees and the sliceable dual graphs? Well, the rectangles are related in exactly the expected way:


Here, the same BSP tree is on the left (without some labels), and the slicea…

Listing Degree Restricted Trees

Although stack overflow is generally just an endless source of questions on the lines of "HALP plz give CODES!? ... NOT homeWORK!! - don't close :(" occasionally you get more interesting ones. For example this one that asks about degree-restricted trees. Also there's some stuff about vertex labelling, but I think I've slightly missed something there.

In any case, lets look at the simpler problem : listing non-isomorphic trees with max degree 3. It's a nice small example of a general approach that I've been thinking about. The idea is to:
Given N vertices, partition 2(N - 1) into N parts of at most 3 -> D = {d0, d1, ... }For each d_i in D, connect the degrees in all possible ways that make trees.Filter out duplicates within each set generated by some d_i. Hmm. Sure would be nice to have maths formatting on blogger....

Anyway, look at this example for partitioning 12 into 7 parts:

At the top are the partitions, in the middle the trees (colored by degree) …

Common Vertex Matrices of Graphs

There is an interesting set of papers out this year by Milan Randic et al (sorry about the accents - blogger seems to have a problem with accented 'c'...). I've looked at his work before here.

[1] Common vertex matrix: A novel characterization of molecular graphs by counting
[2] On the centrality of vertices of molecular graphs

and one still in publication to do with fullerenes. The central idea here (ho ho) is a graph descriptor a bit like path lengths called 'centrality'. Briefly, it is the count of neighbourhood intersections between pairs of vertices. Roughly this is illustrated here:


For the selected pair of vertices, the common vertices are those at the same distance from each - one at a distance of two and one at a distance of three. The matrix element for this pair will be the sum - 2 - and this is repeated for all pairs in the graph. Naturally, this is symmetric:


At the right of the matrix is the row sum (∑) which can be ordered to provide a graph invarian…