






















Abstract:The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by settings such as Model Predictive Control (MPC), where one repeatedly solves related optimization problems with fixed structure and changing parameter values, we propose learning online updates of the relaxation parameter to improve performance on problem classes of interest. This choice is computationally attractive in OSQP-like architectures, since adapting relaxation does not trigger the matrix refactorizations associated with penalty updates. We establish convergence guarantees for ADMM with time-varying penalty and relaxation parameters under mild assumptions, and show on benchmark quadratic programs that the resulting learned policies improve both iteration count and wall-clock time over baseline OSQP.
| Subjects: | Optimization and Control (math.OC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.26932 [math.OC] |
| (or arXiv:2604.26932v1 [math.OC] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26932 arXiv-issued DOI via DataCite (pending registration) |
From: Junan Lin [view email]
[v1]
Wed, 29 Apr 2026 17:45:52 UTC (82 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。