LATIDIA · Investigación

DecompGym: Evaluación comparativa de agentes LLM en el diseño de algoritmos de descomposición

arXiv:2610.38175v1 Anuncio Tipo: nuevo Resumen: La descomposición de Benders y la generación de columnas resuelven grandes problemas de optimización pasando cortes, columnas y valores duales entre un problema maestro y subproblemas, y son

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arXiv:2610.38175v1 Announce Type: new Abstract: Benders decomposition and column generation solve large optimization problems by passing cuts, columns, and dual values between a master problem and subproblems, and are hard to implement correctly. Large language models now automate parts of algorithm design, which points toward agents that build decomposition solvers for new problems. As a first step toward such agents, we study how well current coding agents design and implement these two algorithms, and how they fail. We introduce DecompGym, 81 instances from nine problem families (five Benders, four column generation), in which an agent writes a solver for a prescribed method. Each solver must meet three criteria: execution within