ZDD-Based Algorithmic Framework for Solving Shortest Reconfiguration Problems
Takehiro Ito, Jun Kawahara, Yu Nakahata, Takehide Soh, Akira Suzuki,, Junichi Teruyama, Takahisa Toda

TL;DR
This paper introduces a ZDD-based algorithmic framework to efficiently solve reconfiguration problems, enabling shortest solution transformations and detailed analysis of the solution space, even when exponential in size.
Contribution
The paper presents a novel ZDD-based framework that efficiently explores and analyzes the solution space of reconfiguration problems, including shortest transformation paths.
Findings
Efficiently finds shortest reconfiguration sequences using ZDDs.
Provides detailed information on solution space connectivity.
Demonstrates applicability to various reconfiguration problems.
Abstract
This paper proposes an algorithmic framework for various reconfiguration problems using zero-suppressed binary decision diagrams (ZDDs), a data structure for families of sets. In general, a reconfiguration problem checks if there is a step-by-step transformation between two given feasible solutions (e.g., independent sets of an input graph) of a fixed search problem such that all intermediate results are also feasible and each step obeys a fixed reconfiguration rule (e.g., adding/removing a single vertex to/from an independent set). The solution space formed by all feasible solutions can be exponential in the input size, and indeed many reconfiguration problems are known to be PSPACE-complete. This paper shows that an algorithm in the proposed framework efficiently conducts the breadth-first search by compressing the solution space using ZDDs, and finds a shortest transformation between…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsProduct Development and Customization · Computational Drug Discovery Methods · Multi-Criteria Decision Making
