ADDMC: Weighted Model Counting with Algebraic Decision Diagrams

Dudek, Jeffrey M.; Phan, Vu H. N.; Vardi, Moshe Y.

doi:10.48550/arxiv.1907.05000

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…4. Similar ideas of traversing BDDs bottom-up have been used in BDD-based SAT solving (Pan and Vardi 2006) and weighted model counting (Dudek, Phan, and Vardi 2019). Theorem 6.…”

Section: Gradsat: From Walsh-fourier Expansions To Bddsmentioning

confidence: 97%

On Continuous Local BDD-Based Search for Hybrid SAT Solving

Kyrillidis

Vardi

Zhang

2021

AAAI

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We explore the potential of continuous local search (CLS) in SAT solving by proposing a novel approach for finding a solution of a hybrid system of Boolean constraints. The algorithm is based on CLS combined with belief propagation on binary decision diagrams (BDDs). Our framework accepts all Boolean constraints that admit compact BDDs, including symmetric Boolean constraints and small-coefficient pseudo-Boolean constraints as interesting families. We propose a novel algorithm for efficiently computing the gradient needed by CLS. We study the capabilities and limitations of our versatile CLS solver, GradSAT, by applying it on many benchmark instances. The experimental results indicate that GradSAT can be a useful addition to the portfolio of existing SAT and MaxSAT solvers for solving Boolean satisfiability and optimization problems.

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“…4. Similar ideas of traversing BDDs bottom-up have been used in BDD-based SAT solving (Pan and Vardi 2006) and weighted model counting (Dudek, Phan, and Vardi 2019). Theorem 6.…”

Section: Gradsat: From Walsh-fourier Expansions To Bddsmentioning

confidence: 97%

On Continuous Local BDD-Based Search for Hybrid SAT Solving

Kyrillidis

Vardi

Zhang

2021

AAAI

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“…4. Similar ideas of traversing BDDs bottom-up have been used in BDD-based SAT solving [43] and weighted model counting [44]. Theorem 6.…”

Section: Gradsat: From Walsh-fourier Expansions To Bddsmentioning

confidence: 97%

On Continuous Local BDD-Based Search for Hybrid SAT Solving

Kyrillidis¹,

Vardi²,

Zhang³

2020

Preprint

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“…Dynamic programming (DP) is widely used in Boolean reasoning problems such as satisfiability checking [20,21], Boolean synthesis [22], and model counting [23]. In practice, the DP-based model counter, ADDMC [24] tied for the first place in the weighted track of the 2020 Model Counting Competition [25]. Recently, ADDMC was further enhanced to DPMC [17] by decoupling the planning phase from the execution phase.…”

Section: Related Work Dynamic Programming and Maxsatmentioning

confidence: 99%

DPMS: An ADD-Based Symbolic Approach for Generalized MaxSAT Solving

Kyrillidis¹,

Vardi²,

Zhang³

2022

Preprint

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Boolean MaxSAT, as well as generalized formulations such as Min-MaxSAT and Max-hybrid-SAT, are fundamental optimization problems in Boolean reasoning. Existing methods for MaxSAT have been successful in solving benchmarks in CNF format. They lack, however, the ability to handle hybrid and generalized MaxSAT problems natively. To address this issue, we propose a novel dynamic-programming approach for solving generalized MaxSAT problems -called Dynamic-Programming-MaxSAT or DPMS for short-based on Algebraic Decision Diagrams (ADDs). With the power of ADDs and the (graded) project-join-tree builder, our versatile framework can handle many generalizations of MaxSAT, such as MaxSAT with non-CNF constraints, Min-MaxSAT and MinSAT. Moreover, DPMS scales provably well on instances with low width. Empirical results indicate that DPMS is able to solve certain problems quickly, where other algorithms based on various techniques all fail. Hence, DPMS is a promising framework and opens a new line of research that desires more investigation in the future.

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ADDMC: Weighted Model Counting with Algebraic Decision Diagrams

Cited by 3 publications

References 21 publications

On Continuous Local BDD-Based Search for Hybrid SAT Solving

On Continuous Local BDD-Based Search for Hybrid SAT Solving

On Continuous Local BDD-Based Search for Hybrid SAT Solving

DPMS: An ADD-Based Symbolic Approach for Generalized MaxSAT Solving

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