New SAT-based framework tackles graph labeling problems

Researchers have introduced a new framework for solving Minimum Span Antibandwidth and Cyclic Antibandwidth Labeling (MSABL/MSCABL) problems. These problems are variations of graph labeling tasks that focus on minimizing the span of labels while maintaining a minimum distance between adjacent vertices. The proposed approach utilizes a Boolean Satisfiability (SAT)-based method, which breaks down the problem into a series of decision problems and employs monotonicity to speed up the search. The study also explores parallel and incremental SAT solving strategies, demonstrating their effectiveness and competitiveness against established solvers like CPLEXCP, CPLEXMIP, and Gurobi on benchmark instances. AI

IMPACT Introduces novel computational methods for graph labeling problems, potentially improving efficiency in related AI research areas.

RANK_REASON The cluster describes a new computational approach and framework for solving specific graph labeling problems, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=0.4]

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• boolean satisfiability problem
• CPLEXCP
• CPLEXMIP
• Cyclic Antibandwidth Labeling
• Gurobi
• Harwell-Boeing Sparse Matrix Collection
• Minimum Span Antibandwidth Labeling
• MSABL
• MSCABL

• paper
• other

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