Solver Configuration

Solver configuration aims to optimize the performance of algorithms by selecting the best parameter settings for a given problem instance. Current research focuses on learning-based approaches, employing techniques like machine learning (including graph neural networks and logistic regression) and mathematical programming to predict optimal configurations, often incorporating constraints to handle parameter dependencies. This research is significant because efficient solver configuration can drastically improve the speed and scalability of solving complex problems across diverse fields, from robotics and control systems to operations research and artificial intelligence.