Author name cluster

Jo Devriendt

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5 papers

2 author rows

SAT Conference 2025 Conference Paper

Improving Reduction Techniques in Pseudo-Boolean Conflict Analysis

Orestis Lomis
Jo Devriendt
Hendrik Bierlee
Tias Guns

Recent pseudo-Boolean (PB) solvers leverage the cutting planes proof system to perform SAT-style conflict analysis during search. This process learns implied PB constraints, which can prune later parts of the search tree and is crucial to a PB solver’s performance. A key step in PB conflict analysis is the reduction of a reason constraint, which caused a variable propagation that contributed to the conflict. While necessary, reduction generally makes the reason constraint less strong. Consequently, different approaches to reduction have been proposed, broadly categorised as division- or saturation-based, with the aim of preserving the strength of the reason constraint as much as possible. This paper proposes two novel techniques in each reduction category. We theoretically show how each technique yields reason constraints which are at least as strong as those obtained from existing reduction methods. We then evaluate the empirical effectiveness of the reduction techniques on hard knapsack instances and the most recent PB'24 competition benchmarks.

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AAAI Conference 2021 Conference Paper

Cutting to the Core of Pseudo-Boolean Optimization: Combining Core-Guided Search with Cutting Planes Reasoning

Jo Devriendt
Stephan Gocht
Emir Demirović
Jakob Nordström
Peter J. Stuckey

Core-guided techniques have revolutionized Boolean satisfiability approaches to optimization problems (MaxSAT), but the process at the heart of these methods, strengthening bounds on solutions by repeatedly adding cardinality constraints, remains a bottleneck. Cardinality constraints require significant work to be re-encoded to SAT, and SAT solvers are notoriously weak at cardinality reasoning. In this work, we lift core-guided search to pseudo-Boolean (PB) solvers, which deal with more general PB optimization problems and operate natively with cardinality constraints. The cutting planes method used in such solvers allows us to derive stronger cardinality constraints, which yield better updates to solution bounds, and the increased efficiency of objective function reformulation also makes it feasible to switch repeatedly between lower-bounding and upper-bounding search. A thorough evaluation on applied and crafted benchmarks shows that our core-guided PB solver significantly improves on the state of the art in pseudo-Boolean optimization.

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SAT Conference 2017 Conference Paper

Symmetric Explanation Learning: Effective Dynamic Symmetry Handling for SAT

Jo Devriendt
Bart Bogaerts 0001
Maurice Bruynooghe

Abstract The presence of symmetry in Boolean satisfiability (SAT) problem instances often poses challenges to solvers. Currently, the most effective approach to handle symmetry is by static symmetry breaking, which generates asymmetric constraints to add to the instance. An alternative way is to handle symmetry dynamically during solving. As modern SAT solvers can be viewed as propositional proof generators, adding a symmetry rule in a solver’s proof system would be a straightforward technique to handle symmetry dynamically. However, none of these proposed symmetrical learning techniques are competitive to static symmetry breaking. In this paper, we present symmetric explanation learning, a form of symmetrical learning based on learning symmetric images of explanation clauses for unit propagations performed during search. A key idea is that these symmetric clauses are only learned when they would restrict the current search state, i. e. , when they are unit or conflicting. We further provide a theoretical discussion on symmetric explanation learning and a working implementation in a state-of-the-art SAT solver. We also present extensive experimental results indicating that symmetric explanation learning is the first symmetrical learning scheme competitive with static symmetry breaking.

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SAT Conference 2016 Conference Paper

Improved Static Symmetry Breaking for SAT

Jo Devriendt
Bart Bogaerts 0001
Maurice Bruynooghe
Marc Denecker

Abstract An effective SAT preprocessing technique is the construction of symmetry breaking formulas: auxiliary clauses that guide a SAT solver away from needless exploration of symmetric subproblems. However, during the past decade, state-of-the-art SAT solvers rarely incorporated symmetry breaking. This suggests that the reduction of the search space does not outweigh the overhead incurred by detecting symmetry and constructing symmetry breaking formulas. We investigate three methods to construct more effective symmetry breaking formulas. The first method simply improves the encoding of symmetry breaking formulas. The second detects special symmetry subgroups, for which complete symmetry breaking formulas exist. The third infers binary symmetry breaking clauses for a symmetry group as a whole rather than longer clauses for individual symmetries. We implement these methods in a symmetry breaking preprocessor, and verify their effectiveness on both hand-picked problems as well as the 2014 SAT competition benchmark set. Our experiments indicate that our symmetry breaking preprocessor improves the current state-of-the-art in static symmetry breaking for SAT and has a sufficiently low overhead to improve the performance of modern SAT solvers on hard combinatorial instances.

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IJCAI Conference 2016 Conference Paper

Relevance for SAT(ID)

Joachim Jansen
Bart Bogaerts
Jo Devriendt
Gerda Janssens
Marc Denecker

Inductive definitions and justifications are well-studied concepts. Solvers that support inductive definitions have been developed, but several of their computationally nice properties have never been exploited to improve these solvers. In this paper, we present a new notion called relevance. We determine a class of literals that are relevant for a given definition and partial interpretation, and show that choices on irrelevant atoms can never benefit the search for a model. We propose an early stopping criterion and a modification of existing heuristics that exploit relevance. We present a first implementation in MinisatID and experimentally evaluate our approach, and study how often existing solvers make choices on irrelevant atoms.

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