Publications
Bonassi, Fabio; Xie, Jing; Farina, Marcello; Scattolini, Riccardo
Towards lifelong learning of Recurrent Neural Networks for control design Inproceedings
In: 2022 European Control Conference (ECC), pp. 2018–2023, IEEE 2022.
@inproceedings{Bonassi2022,
title = {Towards lifelong learning of Recurrent Neural Networks for control design},
author = {Fabio Bonassi and Jing Xie and Marcello Farina and Riccardo Scattolini},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 European Control Conference (ECC)},
pages = {2018--2023},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Simpson, Léo; Ghezzi, Andrea; Asprion, Jonas; Diehl, Moritz
Parameter Estimation of Linear Dynamical Systems with Gaussian Noise Working paper
2022.
@workingpaper{Simpson2022,
title = {Parameter Estimation of Linear Dynamical Systems with Gaussian Noise},
author = {Léo Simpson and Andrea Ghezzi and Jonas Asprion and Moritz Diehl},
url = {https://arxiv.org/abs/2211.12302},
doi = {10.48550/ARXIV.2211.12302},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Acerbo, Flavia Sofia; Swevers, Jan; Tuytelaars, Tinne; Son, Tong Duy
Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving Working paper
2022.
@workingpaper{acerboEvaluationMPCbasedImitation2022,
title = {Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving},
author = {Flavia Sofia Acerbo and Jan Swevers and Tinne Tuytelaars and Tong Duy Son},
doi = {10.48550/arXiv.2211.12111},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2211.12111},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Bodard, Alexander; Moran, Ruairi; Schuurmans, Mathijs; Patrinos, Panagiotis; Sopasakis, Pantelis
SPOCK: A Proximal Method for Multistage Risk-Averse Optimal Control Problems Working paper
2022.
@workingpaper{bodardSPOCKProximalMethod2022,
title = {SPOCK: A Proximal Method for Multistage Risk-Averse Optimal Control Problems},
author = {Alexander Bodard and Ruairi Moran and Mathijs Schuurmans and Panagiotis Patrinos and Pantelis Sopasakis},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2212.01110},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Bonassi, Fabio; Scattolini, Riccardo
Recurrent Neural Network-based Internal Model Control Design for Stable Nonlinear Systems Journal Article
In: European Journal of Control, vol. 65, pp. 100632, 2022, ISSN: 0947-3580.
@article{bonassiRecurrentNeuralNetworkbased2022,
title = {Recurrent Neural Network-based Internal Model Control Design for Stable Nonlinear Systems},
author = {Fabio Bonassi and Riccardo Scattolini},
doi = {10.1016/j.ejcon.2022.100632},
issn = {0947-3580},
year = {2022},
date = {2022-01-01},
journal = {European Journal of Control},
volume = {65},
pages = {100632},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Coppens, Peter; Patrinos, Panagiotis
Policy Iteration Using Q-functions: Linear Dynamics with Multiplicative Noise Working paper
2022.
@workingpaper{coppensPolicyIterationUsing2022,
title = {Policy Iteration Using Q-functions: Linear Dynamics with Multiplicative Noise},
author = {Peter Coppens and Panagiotis Patrinos},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2212.01192},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Hermans, Ben; Themelis, Andreas; Patrinos, Panagiotis
QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs Journal Article
In: Math. Prog. Comp., vol. 14, no. 3, pp. 497–541, 2022, ISSN: 1867-2957.
@article{hermansQPALMProximalAugmented2022,
title = {QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs},
author = {Ben Hermans and Andreas Themelis and Panagiotis Patrinos},
doi = {10.1007/s12532-022-00218-0},
issn = {1867-2957},
year = {2022},
date = {2022-01-01},
journal = {Math. Prog. Comp.},
volume = {14},
number = {3},
pages = {497--541},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ionescu, Tudor C.; Bourkhissi, Lahcen El; Necoara, Ion
Least Squares Moment Matching-Based Model Reduction Using Convex Optimization Inproceedings
In: 2022 26th International Conference on System Theory, Control and Computing (ICSTCC), pp. 325–330, 2022, ISSN: 2372-1618.
@inproceedings{ionescuLeastSquaresMoment2022,
title = {Least Squares Moment Matching-Based Model Reduction Using Convex Optimization},
author = {Tudor C. Ionescu and Lahcen El Bourkhissi and Ion Necoara},
doi = {10.1109/ICSTCC55426.2022.9931837},
issn = {2372-1618},
year = {2022},
date = {2022-01-01},
booktitle = {2022 26th International Conference on System Theory, Control and Computing (ICSTCC)},
pages = {325--330},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Lahr, Amon; Zanelli, Andrea; Carron, Andrea; Zeilinger, Melanie N.
Zero-Order Optimization for Gaussian Process-based Model Predictive Control Working paper
2022.
@workingpaper{lahrZeroOrderOptimizationGaussian2022,
title = {Zero-Order Optimization for Gaussian Process-based Model Predictive Control},
author = {Amon Lahr and Andrea Zanelli and Andrea Carron and Melanie N. Zeilinger},
doi = {10.48550/arXiv.2211.15522},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2211.15522},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Pas, Pieter; Schuurmans, Mathijs; Patrinos, Panagiotis
Alpaqa: A Matrix-Free Solver for Nonlinear MPC and Large-Scale Nonconvex Optimization Inproceedings
In: 2022 European Control Conference (ECC), pp. 417–422, 2022.
@inproceedings{pasAlpaqaMatrixfreeSolver2022,
title = {Alpaqa: A Matrix-Free Solver for Nonlinear MPC and Large-Scale Nonconvex Optimization},
author = {Pieter Pas and Mathijs Schuurmans and Panagiotis Patrinos},
doi = {10.23919/ECC55457.2022.9838172},
year = {2022},
date = {2022-01-01},
booktitle = {2022 European Control Conference (ECC)},
pages = {417--422},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pethick, Thomas; Latafat, Puya; Patrinos, Panagiotis; Fercoq, Olivier; Cevher, Volkan
Escaping Limit Cycles: Global Convergence for Constrained Nonconvex-Nonconcave Minimax Problems Inproceedings
In: International Conference on Learning Representations, online, France, 2022.
@inproceedings{pethickEscapingLimitCycles2022,
title = {Escaping Limit Cycles: Global Convergence for Constrained Nonconvex-Nonconcave Minimax Problems},
author = {Thomas Pethick and Puya Latafat and Panagiotis Patrinos and Olivier Fercoq and Volkan Cevher},
year = {2022},
date = {2022-01-01},
booktitle = {International Conference on Learning Representations},
address = {online, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rotaru, Teodor; cois Glineur, Franc; Patrinos, Panagiotis
Tight Convergence Rates of the Gradient Method on Smooth Hypoconvex Functions Working paper
2022.
@workingpaper{rotaruTightConvergenceRates2022,
title = {Tight Convergence Rates of the Gradient Method on Smooth Hypoconvex Functions},
author = {Teodor Rotaru and Franc cois Glineur and Panagiotis Patrinos},
doi = {10.48550/arXiv.2203.00775},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2203.00775},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Schuurmans, Mathijs; Katriniok, Alexander; Meissen, Christopher; Tseng, H. Eric; Patrinos, Panagiotis
Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach Working paper
2022.
@workingpaper{schuurmansSafeLearningBasedMPC2022,
title = {Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach},
author = {Mathijs Schuurmans and Alexander Katriniok and Christopher Meissen and H. Eric Tseng and Panagiotis Patrinos},
doi = {10.48550/arXiv.2206.13319},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2206.13319},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Themelis, Andreas; Stella, Lorenzo; Patrinos, Panagiotis
Douglas–Rachford Splitting and ADMM for Nonconvex Optimization: Accelerated and Newton-type Linesearch Algorithms Journal Article
In: Comput Optim Appl, vol. 82, no. 2, pp. 395–440, 2022, ISSN: 1573-2894.
@article{themelisDouglasRachfordSplitting2022,
title = {Douglas–Rachford Splitting and ADMM for Nonconvex Optimization: Accelerated and Newton-type Linesearch Algorithms},
author = {Andreas Themelis and Lorenzo Stella and Panagiotis Patrinos},
doi = {10.1007/s10589-022-00366-y},
issn = {1573-2894},
year = {2022},
date = {2022-01-01},
journal = {Comput Optim Appl},
volume = {82},
number = {2},
pages = {395--440},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Løwenstein, Kristoffer Fink; Fagiano, Lorenzo; Bernardini, Daniele; Bemporad, Alberto
Physics-Informed Online Learning of Gray-box Models by Moving Horizon Estimation Working paper
2022, (Under review).
@workingpaper{Lowenstein2022PhysicsInformed,
title = {Physics-Informed Online Learning of Gray-box Models by Moving Horizon Estimation},
author = {Kristoffer Fink Løwenstein and Lorenzo Fagiano and Daniele Bernardini and Alberto Bemporad},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
abstract = {A simple yet expressive prediction model is an essential ingredient in model-based control and estimation. Models derived from fundamental physical principles may fail to capture the complexity of the actual system dynamics. A potential solution is the use of a physics-informed, or gray-box model that extends a physics-based model with a data-driven part. Learning the latter might be challenging, due to noisy measurements and lack of full state information. This work presents a method based on Moving Horizon Estimation (MHE) for simultaneous state estimation and training of a neural network submodel. The method can be used in offline training or applied online for adaptation without any prior knowledge than the white-box submodel. We analyze the capabilities of the method in a two degree of freedom robotic manipulator case study, also showing how it can be used for online adaptation to cope with a time-varying model mismatch},
note = {Under review},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Wang, Jianhong; Wang, Jinxin; Zhang, Yuan; Gu, Yunjie; Kim, Tae-Kyun
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning Working paper
2021.
@workingpaper{wang2021shaq,
title = {SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning},
author = {Jianhong Wang and Jinxin Wang and Yuan Zhang and Yunjie Gu and Tae-Kyun Kim},
url = {https://arxiv.org/abs/2105.15013},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {arXiv preprint arXiv:2105.15013},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Laude, Emanuel; Themelis, Andreas; Patrinos, Panagiotis
Conjugate Dualities for Relative Smoothness and Strong Convexity under the Light of Generalized Convexity Working paper
2021.
@workingpaper{laudeConjugateDualitiesRelative2021,
title = {Conjugate Dualities for Relative Smoothness and Strong Convexity under the Light of Generalized Convexity},
author = {Emanuel Laude and Andreas Themelis and Panagiotis Patrinos},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
number = {arXiv:2112.08886},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}