no code implementations • 19 Jul 2022 • Ahmed Aboudonia, Goran Banjac, Annika Eichler, John Lygeros
A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline.
no code implementations • 31 Dec 2021 • Angeliki Kamoutsi, Goran Banjac, John Lygeros
We consider large-scale Markov decision processes (MDPs) with an unknown cost function and employ stochastic convex optimization tools to address the problem of imitation learning, which consists of learning a policy from a finite set of expert demonstrations.
no code implementations • 28 Dec 2021 • Angeliki Kamoutsi, Goran Banjac, John Lygeros
We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations.
1 code implementation • NeurIPS 2021 • Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E. Gonzalez, Ion Stoica, Ken Goldberg
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.
no code implementations • 1 Jul 2020 • Ahmed Aboudonia, Annika Eichler, Francesco Cordiano, Goran Banjac, John Lygeros
The proposed scheme is tested in simulation where the proposed MPC problem is solved using distributed optimization.
1 code implementation • 9 Dec 2019 • Michel Schubiger, Goran Banjac, John Lygeros
The alternating direction method of multipliers (ADMM) is a powerful operator splitting technique for solving structured convex optimization problems.
Optimization and Control
2 code implementations • 21 Nov 2017 • Bartolomeo Stellato, Goran Banjac, Paul Goulart, Alberto Bemporad, Stephen Boyd
We present a general purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration.
Optimization and Control