Learning to adapt relaxation parameters in ADMM can speed up solving repeated optimization problems while maintaining convergence guarantees—useful for real-time control systems that solve similar problems repeatedly.
This paper shows how to use machine learning to automatically tune the relaxation parameter in ADMM, an algorithm for solving optimization problems. By learning better parameter choices for repeated similar problems (like in Model Predictive Control), the method reduces computation time without requiring expensive matrix refactorizations.