arXiv:2606. 06656v1 Announce Type: new Abstract: We study parallel Continuous Local Search (CLS) as a solution approach for Boolean satisfiability problems with symmetric pseudo-Boolean (PB) constraints.
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arXiv:2606. 06641v1 Announce Type: new Abstract: We present Accelerated Fourier SAT (AFSAT), a GPU-accelerated solver for pseudo-Boolean satisfiability based on continuous local search (CLS).
The paper introduces the Minimum Span Antibandwidth and Cyclic Antibandwidth Labeling (MSABL/MSCABL) problems, which fix a minimum (cyclic) distance between labels of adjacent vertices and aim to minimize the overall label span. A unified Boolean Satisfiability (SAT) framework is developed, formulating the problems as a sequence of decision problems and exploiting monotonicity to accelerate search. Two SAT solving strategies—parallel and incremental—are evaluated on benchmark instances, showing that SAT-based approaches are highly competitive with commercial solvers, especially for MSCABL.
The paper introduces the Minimum Span Antibandwidth and Cyclic Antibandwidth Labeling (MSABL/MSCABL) problems, which fix a minimum (cyclic) distance between adjacent vertex labels and aim to minimize the overall label span. A unified Boolean Satisfiability (SAT) framework is developed, formulating the problems as a sequence of decision problems and exploiting monotonicity to accelerate search. Two SAT solving strategies—parallel and incremental—are evaluated on benchmark instances from the Harwell‑Boeing Sparse Matrix Collection and compared with commercial solvers, showing that SAT-based approaches are highly competitive, with the parallel method best for MSCABL and the incremental method best for MSABL.
arXiv:2608. 15143v1 Announce Type: new Abstract: Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems.
arXiv:2507. 22876v2 Announce Type: replace Abstract: The Satisfiability problem (SAT) is fundamental in computational complexity theory and has a wide range of industrial applications.
arXiv:2608. 19953v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making.