19 research outputs found
Exploiting Resolution-based Representations for MaxSAT Solving
Most recent MaxSAT algorithms rely on a succession of calls to a SAT solver
in order to find an optimal solution. In particular, several algorithms take
advantage of the ability of SAT solvers to identify unsatisfiable subformulas.
Usually, these MaxSAT algorithms perform better when small unsatisfiable
subformulas are found early. However, this is not the case in many problem
instances, since the whole formula is given to the SAT solver in each call. In
this paper, we propose to partition the MaxSAT formula using a resolution-based
graph representation. Partitions are then iteratively joined by using a
proximity measure extracted from the graph representation of the formula. The
algorithm ends when only one partition remains and the optimal solution is
found. Experimental results show that this new approach further enhances a
state of the art MaxSAT solver to optimally solve a larger set of industrial
problem instances
Approximation Strategies for Incomplete MaxSAT
Incomplete MaxSAT solving aims to quickly find a solution
that attempts to minimize the sum of the weights of the unsati
sfied soft
clauses without providing any optimality guarantees. In th
is paper, we
propose two approximation strategies for improving incomp
lete MaxSAT
solving. In one of the strategies, we cluster the weights and
approximate
them with a representative weight. In another strategy, we b
reak up
the problem of minimizing the sum of weights of unsatisfiable
clauses
into multiple minimization subproblems. Experimental res
ults show that
approximation strategies can be used to find better solution
s than the
best incomplete solvers in the MaxSAT Evaluation 2017
Approximation Strategies for Incomplete MaxSAT
Incomplete MaxSAT solving aims to quickly find a solution that attempts to
minimize the sum of the weights of the unsatisfied soft clauses without
providing any optimality guarantees.
In this paper, we propose two approximation strategies for improving
incomplete MaxSAT solving. In one of the strategies, we cluster the weights and
approximate them with a representative weight. In another strategy, we break up
the problem of minimizing the sum of weights of unsatisfiable clauses into
multiple minimization subproblems. Experimental results show that approximation
strategies can be used to find better solutions than the best incomplete
solvers in the MaxSAT Evaluation 2017.Comment: 10 pages, 3 algorithms, 1 figure, International Conference on
Principles and Practice of Constraint Programming (CP) 201
On Tackling the Limits of Resolution in SAT Solving
The practical success of Boolean Satisfiability (SAT) solvers stems from the
CDCL (Conflict-Driven Clause Learning) approach to SAT solving. However, from a
propositional proof complexity perspective, CDCL is no more powerful than the
resolution proof system, for which many hard examples exist. This paper
proposes a new problem transformation, which enables reducing the decision
problem for formulas in conjunctive normal form (CNF) to the problem of solving
maximum satisfiability over Horn formulas. Given the new transformation, the
paper proves a polynomial bound on the number of MaxSAT resolution steps for
pigeonhole formulas. This result is in clear contrast with earlier results on
the length of proofs of MaxSAT resolution for pigeonhole formulas. The paper
also establishes the same polynomial bound in the case of modern core-guided
MaxSAT solvers. Experimental results, obtained on CNF formulas known to be hard
for CDCL SAT solvers, show that these can be efficiently solved with modern
MaxSAT solvers
Subsumed Label Elimination for Maximum Satisfiability
Proceeding volume: 285Peer reviewe
Boosting Answer Set Optimization with Weighted Comparator Networks
Answer set programming (ASP) is a paradigm for modeling knowledge intensive
domains and solving challenging reasoning problems. In ASP solving, a typical
strategy is to preprocess problem instances by rewriting complex rules into
simpler ones. Normalization is a rewriting process that removes extended rule
types altogether in favor of normal rules. Recently, such techniques led to
optimization rewriting in ASP, where the goal is to boost answer set
optimization by refactoring the optimization criteria of interest. In this
paper, we present a novel, general, and effective technique for optimization
rewriting based on comparator networks, which are specific kinds of circuits
for reordering the elements of vectors. The idea is to connect an ASP encoding
of a comparator network to the literals being optimized and to redistribute the
weights of these literals over the structure of the network. The encoding
captures information about the weight of an answer set in auxiliary atoms in a
structured way that is proven to yield exponential improvements during
branch-and-bound optimization on an infinite family of example programs. The
used comparator network can be tuned freely, e.g., to find the best size for a
given benchmark class. Experiments show accelerated optimization performance on
several benchmark problems.Comment: 36 page
MaxSAT Evaluation 2021 : Solver and Benchmark Descriptions
Non peer reviewe