TL;DR
This work introduces amomaximize, a novel maximization statement that integrates AMO constraints directly into the objective function, and shows that, in specific scenarios, this approach improves performance compared to clingo.
This paper proposes and formalizes two new minimization algorithms that guarantee subset-minimal reasons and ensures cardinality-minimal reasons in the AMOSUM constraint and demonstrates that extending the solver wasp with these minimization strategies leads to substantial performance improvements.
This work systematically investigates whether LLMs can identify problem structure from natural-language descriptions and apply suitable optimization modeling techniques to generate mathematical models and solver code that solve the problems correctly and efficiently.
A solver-informed diagnosis mechanism that exploits fine-grained solver statistics as verbal gradients for targeted refinement and a structured memory abstracts prior experience into reusable modeling strategies, avoiding redundant exploration while enabling zero-shot transfer to unseen problems and bootstrapping small...
SAILOR, a proof-of-concept system that detects such unsupported numerical choices, asks the user targeted follow-up questions, and updates the optimization model before returning a solution, is presented.
This paper introduces AESAT (Auto-Evolving SAT solving), a novel neuro-symbolic framework designed to automatically evolve and optimize the heuristic functions of SAT solvers, and represents the first time an LLM-enhanced solver has dominated the world's premier SAT competition.
Answer Set Programming (ASP) stands as a powerful, declarative paradigm within knowledge-driven AI, offering a robust framework for complex search and optimization. Unlike data-driven methods, ASP leverages explicit knowledge representation to provide guarantees on correctness and optimality. This paper chronicles...
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