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.
Optimization modeling formulates real-world decision problems as mathematical programs that solvers can use to find optimal decisions. Large language models (LLMs) can automate this process, but the resulting correct formulations can require substantial time and memory to construct and solve, limiting practical scalabi...
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...
Natural-language descriptions of optimization problems may be incomplete or vague about numerical information that a solver requires, including costs, capacities, demands, bounds, and penalties. A language model can translate the description into code, but when a required value is absent it must either stop or guess. W...
Despite decades of intensive research and optimization, modern Boolean Satisfiability (SAT) solvers have reached a plateau where significant performance gains are increasingly difficult to achieve. While Large Language Models (LLMs) have demonstrated remarkable capabilities in pattern recognition and code generation fo...
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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