3. Efficient Algorithms
These problems concern the design of efficient algorithms for various tasks related to partition sampling and counting, as well as specific computations.-
#3 Enumerating partitions with ZDD
Currently, the best known algorithm for enumerating connected, balanced graph partitions is based on Zero-suppressed Decision Diagrams, or ZDD for short [Nakahata, Kawahara, and Kasahara, 2018].Problem 3.1.
[Gabe Schoenbach, Daryl DeFord] Is ZDD the best algorithm to enumerate all balanced partitions that are $\varepsilon$-balanced? Or is there some other method?
(a) Is there a combinatorial/geometric way to understand the pruned branches in the ZDD algorithm?
(b) Can we analyze ZDD to get better bounds on the likelihood of approximate splittability of random trees? -
#8 UST speedup
Problem 3.2.
[Gregory Herschlag] Is there a faster algorithm to draw an (approximately) uniformly random spanning tree, perhaps on well-behaved graph families? -
#10 Uniform partition sampling
Problem 3.3.
[org.aimpl.user:jtuckerfoltz@gmail.com] Can we sample a uniformly random balanced graph partition into two pieces in polynomial time on well-behaved graphs? Specifically, can we do this on grid graphs? Wesley Pegden has an informal argument suggesting that simply running the flip walk would not mix in polynomial-time, so that does not work. Also, what if we drop the balance constraint? -
#22 Recursive splitting balance constraints
Problem 3.4.
[Jeanne Clelland] When recursively splitting trees to build a balanced partition, what population balance constraints should be enforced in the earlier steps to get approximate balance at the end? -
#31 Most likely MST on grids
Problem 3.5.
[Kris Tapp] Can we find the MST probability maximizer (with iid random weights) in, say, an $8 \times 8$ grid graph? Can we approximately optimize this numerically?
Cite this as: AimPL: Mathematical foundations of sampling connected balanced graph partitions, available at http://aimpl.org/connectedbalanced.