reconfillion 1.0.1

Solving combinatorial reconfiguration problems.

Reconfillion - Python interface for combinatorial reconfiguration problems

Reconfillion was released as version 1.0.0 on April 22, 2024. The older version of reconfillion before that date exists on https://github.com/junkawahara/reconfillion-kari , but is not compatible with this version.

Reconfillion is a tool for solving combinatorial reconfiguration problems. It works with graphillion, which means that combinatorial reconfiguration problems of graph classes that are supported by graphillion can be solved by reconfillion.

Requirements

• Graphillion version v1.7 or higher is needed.

License

MIT License

Quick install

You can install reconfillion via pip.

pip install reconfillion

Tutorial

Let's consider to solve the spanning tree reconfiguration problem. In reconfillion (and graphillion), an edge is represented by a tuple of two vertices, and a graph is represented by a list of edges.

# complete graph with 4 vertices [1, 2, 3, 4]
graph = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]

We import graphillion and reconfillion, and make GraphSet of all the spanning trees on the graph.

from graphillion import GraphSet
from reconfillion import reconf

GraphSet.set_universe(graph) # See the graphillion manual.
spanning_trees = GraphSet.trees(is_spanning = True)

Then, by doing the following method, we can obtain the reconfiguration sequence between s and t.

s = [(1, 2), (1, 3), (1, 4)] # start spanning tree
t = [(1, 4), (2, 4), (3, 4)] # goal spanning tree

# obtain a reconfiguration sequence between s and t under the token jumping model.
reconf_sequence = reconf.get_reconf_seq(s, t, spanning_trees, model = 'tj')

# obtained [[(1, 4), (2, 4), (3, 4)], [(1, 2), (1, 4), (2, 4)], [(1, 2), (1, 3), (1, 4)]]

Note

This software (and graphillion) needs a lot of memory to solve problems with large-size instances.

Authors

Reconfillion has been developed by Jun Kawahara and Hiroki Yamazaki.

Acknowledgment

This project is/was partially supported by JSPS KAKENHI Grant Numbers JP18H04091, JP20H05794, and JP23H04383.