Mixsatgen: Learning graph mixing for sat instance generation
The Boolean satisfiability problem (SAT) stands as a canonical NP-complete task. In
particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT …
particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT …
Rethinking Branching on Exact Combinatorial Optimization Solver: The First Deep Symbolic Discovery Framework
Machine learning (ML) has been shown to successfully accelerate solving NP-hard
combinatorial optimization (CO) problems under the branch and bound framework …
combinatorial optimization (CO) problems under the branch and bound framework …
L2P-MIP: Learning to presolve for mixed integer programming
Modern solvers for solving mixed integer programming (MIP) often rely on the branch-and-
bound (B&B) algorithm which could be of high time complexity, and presolving techniques …
bound (B&B) algorithm which could be of high time complexity, and presolving techniques …
Large Language Models are Good Multi-lingual Learners: When LLMs Meet Cross-lingual Prompts
With the advent of Large Language Models (LLMs), generating rule-based data for real-
world applications has become more accessible. Due to the inherent ambiguity of natural …
world applications has become more accessible. Due to the inherent ambiguity of natural …
Learning Plaintext-Ciphertext Cryptographic Problems via ANF-based SAT Instance Representation
X Zheng, Y Li, C Fan, H Wu, X Song… - The Thirty-eighth Annual …, 2024 - openreview.net
Cryptographic problems, operating within binary variable spaces, can be routinely
transformed into Boolean Satisfiability (SAT) problems regarding specific cryptographic …
transformed into Boolean Satisfiability (SAT) problems regarding specific cryptographic …
Improving Machine Learning Efficiency through Dynamic Resource Allocation and Robust Recognition
E Brown, M Johnson, J Smith, A Deshmukh… - Authorea …, 2024 - techrxiv.org
Machine learning efficiency can often be hindered by static resource allocation and
recognition limitations. To tackle these concerns, we propose a dynamic resource allocation …
recognition limitations. To tackle these concerns, we propose a dynamic resource allocation …
[PDF][PDF] Hybrid Optimization Strategies Combining Genetic Algorithms with Machine Learning for Graph Problems
L Zhang, X Chen, Z Sun, G Wang, J Liu, M Zhou - researchgate.net
Large-scale optimization problems in graph theory present considerable challenges that
necessitate innovative approaches for efficient resolution. Traditional Genetic Algorithms …
necessitate innovative approaches for efficient resolution. Traditional Genetic Algorithms …