UniCO: Towards a Unified Model for Combinatorial Optimization Problems

• URL: http://arxiv.org/abs/2505.06290v1
• Date: Wed, 07 May 2025 12:39:33 GMT
• Title: UniCO: Towards a Unified Model for Combinatorial Optimization Problems
• Authors: Zefang Zong, Xiaochen Wei, Guozhen Zhang, Chen Gao, Huandong Wang, Yong Li,
• Abstract summary: Combinatorial Optimization (CO) encompasses a wide range of problems that arise in many real-world scenarios.<n>We introduce UniCO, a unified model for solving various CO problems.
• Score: 30.524941693870534
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Combinatorial Optimization (CO) encompasses a wide range of problems that arise in many real-world scenarios. While significant progress has been made in developing learning-based methods for specialized CO problems, a unified model with a single architecture and parameter set for diverse CO problems remains elusive. Such a model would offer substantial advantages in terms of efficiency and convenience. In this paper, we introduce UniCO, a unified model for solving various CO problems. Inspired by the success of next-token prediction, we frame each problem-solving process as a Markov Decision Process (MDP), tokenize the corresponding sequential trajectory data, and train the model using a transformer backbone. To reduce token length in the trajectory data, we propose a CO-prefix design that aggregates static problem features. To address the heterogeneity of state and action tokens within the MDP, we employ a two-stage self-supervised learning approach. In this approach, a dynamic prediction model is first trained and then serves as a pre-trained model for subsequent policy generation. Experiments across 10 CO problems showcase the versatility of UniCO, emphasizing its ability to generalize to new, unseen problems with minimal fine-tuning, achieving even few-shot or zero-shot performance. Our framework offers a valuable complement to existing neural CO methods that focus on optimizing performance for individual problems.

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