Summary of Resource-constrained Heuristic For Max-sat, by Brian Matejek et al.
Resource-Constrained Heuristic for Max-SAT
by Brian Matejek, Daniel Elenius, Cale Gentry, David Stoker, Adam Cobb
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed resource-constrained heuristic for Max-SAT iteratively decomposes larger problems into smaller subcomponents, leveraging optimized solvers and hardware. The unconstrained outer loop maintains the state space of a problem, selecting subsets of SAT variables for optimization independent of previous calls. A resource-constrained inner loop maximizes the number of satisfiable clauses in the “sub-SAT” problem, with flexibility to use traditional solvers or specialized hardware like QUBO optimizers. The approach selects a subset of SAT variables before QUBO optimization, differing from existing solutions that convert SAT instances to QUBO first. Variable selection methods, including a novel graph-based technique exploiting SAT instance structure, are analyzed and empirically demonstrated on randomly generated Max-SAT instances and real-world examples from the Max-SAT evaluation benchmarks, outperforming existing QUBO decomposer solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to solve difficult math problems. It breaks down big problems into smaller ones that can be solved using computers or special hardware. The approach is flexible and allows for different ways of solving each small problem. The authors test their method on many examples and show it works better than other methods. |
Keywords
» Artificial intelligence » Optimization
