scholarly journals Non-clausal Redundancy Properties

Author(s):  
Lee A. Barnett ◽  
Armin Biere

AbstractState-of-the-art refutation systems for SAT are largely based on the derivation of clauses meeting some redundancy criteria, ensuring their addition to a formula does not alter its satisfiability. However, there are strong propositional reasoning techniques whose inferences are not easily expressed in such systems. This paper extends the redundancy framework beyond clauses to characterize redundancy for Boolean constraints in general. We show this characterization can be instantiated to develop efficiently checkable refutation systems using redundancy properties for Binary Decision Diagrams (BDDs). Using a form of reverse unit propagation over conjunctions of BDDs, these systems capture, for instance, Gaussian elimination reasoning over XOR constraints encoded in a formula, without the need for clausal translations or extension variables. Notably, these systems generalize those based on the strong Propagation Redundancy (PR) property, without an increase in complexity.

Author(s):  
Masaaki Nishino ◽  
Norihito Yasuda ◽  
Kengo Nakamura

Exact cover refers to the problem of finding subfamily F of a given family of sets S whose universe is D, where F forms a partition of D. Knuth’s Algorithm DLX is a state-of-the-art method for solving exact cover problems. Since DLX’s running time depends on the cardinality of input S, it can be slow if S is large. Our proposal can improve DLX by exploiting a novel data structure, DanceDD, which extends the zero-suppressed binary decision diagram (ZDD) by adding links to enable efficient modifications of the data structure. With DanceDD, we can represent S in a compressed way and perform search in linear time with the size of the structure by using link operations. The experimental results show that our method is an order of magnitude faster when the problem is highly compressed.


10.29007/hh3v ◽  
2019 ◽  
Author(s):  
Michał Karpiński ◽  
Marek Piotrów

A Pseudo-Boolean (PB) constraint is a linear inequality constraint over Boolean variables. A popular idea to solve PB-constraints is to transform them to CNFs (via BDDs, adders and sorting networks [5, 11]) and process them using – increasingly improving – state-of-the-art SAT-solvers. Recent research have favored the approach that uses Binary Decision Diagrams (BDDs), which is evidenced by several new constructions and optimizations [2, 21]. We show that encodings based on comparator networks can still be very competitive. We present a system description of a PB-solver based on MiniSat+ [11] which we extended by adding a new construction of selection network called 4-Way Merge Selection Network, with a few optimizations based on other solvers. Experiments show that on many instances of popular benchmarks our technique outperforms other state-of-the-art PB-solvers.


2021 ◽  
Author(s):  
Gianpiero Cabodi ◽  
Paolo E. Camurati ◽  
Alexey Ignatiev ◽  
Joao Marques-Silva ◽  
Marco Palena ◽  
...  

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