Efficient and Reliable Hitting-Set Computations for the Implicit Hitting Set Approach
Hannes Ihalainen, Dieter Vandesande, Andr\'e Schidler, Jeremias Berg, Bart Bogaerts, Matti J\"arvisalo

TL;DR
This paper investigates alternative algorithms for hitting set computations within the implicit hitting set framework, comparing their efficiency and reliability, especially focusing on pseudo-Boolean reasoning and stochastic local search methods.
Contribution
It introduces and evaluates new algorithmic techniques for hitting set optimization, demonstrating that PB reasoning can be competitive and provide correctness certificates.
Findings
Commercial IP solvers are highly effective but may face correctness issues due to numerical instability.
PB reasoning-based hitting set computations can be made competitive with exact IP solvers.
PB reasoning allows for correctness certificates applicable across various IHS instantiations.
Abstract
The implicit hitting set (IHS) approach offers a general framework for solving computationally hard combinatorial optimization problems declaratively. IHS iterates between a decision oracle used for extracting sources of inconsistency and an optimizer for computing so-called hitting sets (HSs) over the accumulated sources of inconsistency. While the decision oracle is language-specific, the optimizers is usually instantiated through integer programming. We explore alternative algorithmic techniques for hitting set optimization based on different ways of employing pseudo-Boolean (PB) reasoning as well as stochastic local search. We extensively evaluate the practical feasibility of the alternatives in particular in the context of pseudo-Boolean (0-1 IP) optimization as one of the most recent instantiations of IHS. Highlighting a trade-off between efficiency and reliability, while a…
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
Taxonomy
TopicsModel Reduction and Neural Networks · Computational Geometry and Mesh Generation · Computer Graphics and Visualization Techniques
