This article presents MOCO, a learnable meta optimizer for combinatorial optimization problems, which are often NP-hard. Traditional methods use handcrafted heuristics, but advances in neural networks have motivated the development…
Continue readingThe article presents a new model-based paradigm, Domain-Independent Dynamic Programming (DIDP), designed to solve combinatorial optimization problems. This approach is based on dynamic programming (DP), traditionally used as a problem-specific…
Continue readingThis paper presents a systematic study on the cybersecurity effectiveness of blending honeypot computers into production computers. The authors formalize the Honeypot Deployment (HD) problem as a combinatorial optimization problem,…
Continue readingThis research investigates the success of message-passing graph neural networks (MPNNs) in solving linear optimization problems. The authors demonstrate that MPNNs can mimic standard interior-point methods for these problems, which…
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