Sparse Convex Quantile Regression: A Generalized Benders Decomposition Approach

Abstract: We develop a scalable algorithmic framework for sparse convex quantile regression (SCQR), addressing key computational challenges in the literature. Enhancing the classical CQR model, we introduce L2-norm regularization and an epsilon-insensitive zone to improve generalization and mitigate overfitting - both theoretically justified and empirically validated. Based on this extension, we improve the SCQR model and propose the first Generalized Benders Decomposition (GBD) algorithm tailored to this context, further strengthened by a novel local search-based Benders matheuristic. Extensive simulations and a real-world application to Sustainable Development Goals benchmarking demonstrate the accuracy, scalability, and practical value of our approach.