{"ID":2841921,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11870","arxiv_id":"2511.11870","title":"Graph-Based Imitation and Reinforcement Learning for Efficient Benders Decomposition","abstract":"This work introduces an end-to-end graph-based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network which takes as input a bipartite graph representation of the master problem and proposes a candidate solution. The agent is trained using a two-stage approach that combines imitation (IL) and reinforcement learning (RL). IL is used to mimic a solver and obtain a warm-start policy which is then finetuned using RL with a reward signal that balances feasibility and computational efficiency. We augment the agent with a verification mechanism that checks the agent's prediction for feasibility and solution quality. The framework is evaluated in two case studies: (i) an illustrative mixed-integer nonlinear program, where it reduces the solution time by 42% without loss of solution quality, and (ii) a closed-loop irrigation scheduling problem, where it achieves a 23% time reduction without compromising water use efficiency.","short_abstract":"This work introduces an end-to-end graph-based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network which takes as input a bipartite graph representation of the master problem and proposes a candidate solution. The agent is trained u...","url_abs":"https://arxiv.org/abs/2511.11870","url_pdf":"https://arxiv.org/pdf/2511.11870v1","authors":"[\"Bernard T. Agyeman\",\"Zhe Li\",\"Ilias Mitrai\",\"Prodromos Daoutidis\"]","published":"2025-11-14T21:00:26Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[\"Graph Neural Network\",\"Reinforcement Learning\"]","has_code":false}