Combinatorial optimization (CO) problems are pivotal across various industrial applications, where the speed of solving these problems is crucial. Improving the performance of CO solvers across diverse input instances requires fine-tuning solver parameters for each instance. However, this tuning process is time-consuming, and the time required increases with the number of instances. To address this, a method called instance-specific algorithm configuration (ISAC) has been devised. This approach involves two main steps: training and execution. During the training step, features are extracted from various instances and then grouped into clusters. For each cluster, parameters are fine-tuned. This cluster-specific tuning process results in a set of generalized parameters for instances belonging to each class. In the execution step, features are extracted from an unknown instance to determine its cluster, and the corresponding pre-tuned parameters are applied. Generally, the running time of a solver is evaluated by the time to solution ($TTS$). However, methods like ISAC require preprocessing. Therefore, the total execution time is $T_{tot}=TTS+T_{tune}$, where $T_{tune}$ represents the tuning time. While the goal is to minimize $T_{tot}$, it is important to note that extracting features in the ISAC method requires a certain amount of computational time. The extracting features include summary statistics of the solver execution logs, which takes several 10 seconds. This research presents a method to significantly reduce the time of the ISAC execution step by streamlining feature extraction and class determination with a graph neural network. Experimental results show that $T_{tune}$ in the execution step, which take several 10 seconds in the original ISAC manner, could be reduced to sub-seconds.
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Leibniz University Hannover（汉诺威莱布尼茨大学）；Jožef Stefan International Postgraduate School（约热夫·斯泰凡国际研究生学院）；Jožef Stefan Institute（约热夫·斯泰凡研究所）；L3S Research Center（L3S研究中心）
GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）
Automated Algorithm Selection for Radar Network Configuration
Thales Research & Technology（泰雷兹研究与技术公司）；École Polytechnique（巴黎综合理工学院）；Institut Polytechnique de Paris（巴黎理工学院）；Institut Pasteur（巴斯德研究所）；Université Paris Cité（巴黎西岱大学）；Thales Land & Air Systems（泰雷兹陆空系统公司）；Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）
Fast and Efficient Matching Algorithm with Deadline Instances
Simons Institute for the Theory of Computing, UC Berkeley（加州大学伯克利分校西蒙斯理论计算研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Rice University（莱斯大学）
