Set-Based Computing Methods in Learning and Control
Event details
| Date | 02.08.2023 |
| Hour | 11:00 › 12:00 |
| Speaker | Prof. Boris Houska (ShanghaiTech University, China) |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract:
Set-based computing methods are the foundation of validated arithmetics, reliable computing, uncertainty propagation, computational configuration analysis, optimization of control invariant sets, robust control, hybrid system analysis, safe learning and dual control, multi-objective optimization, ambiguity analysis, and many others. Therefore, the first part of this talk is about recent breakthroughs in the field of set-based computing. In detail, we focus on a novel class of configuration-constrained polytopes that admit a joint affine parameterization of their vertices and facets. This parameterization can be used to optimize the location and geometric shapes of potentially high dimensional polytopes with millions of facets and vertices by relying on a large-scale linear program (LP) solver. And the second part of this talk discusses applications of such configuration-constrained polytopes in the context of robust model predictive control, set-theoretic dissipativity theory, information-theoretic safe learning, as well as polytopic dual control. The talk concludes with a general assessment of the state of the art in set-based computing and highlights important avenues for future research.
Bio:
Boris Houska is an associate professor at the School of Information Science and Technology at ShanghaiTech University. His research interests include numerical optimization and optimal control, robust and global optimization, as well as fast model predictive control algorithms.
Boris Houska received a diploma in mathematics and physics from the University of Heidelberg in 2007, and a Ph.D. in Electrical Engineering from KU Leuven in 2011. From 2012 to 2013 he was a postdoctoral researcher at the Centre for Process Systems Engineering at Imperial College London. Subsequently, from 2013-14, he has worked as a research faculty member at Shanghai Jiao Tong University. Moreover, he has held visiting professor positions at the Freiburg Institute for Advanced Studies as well as at the Institute for Microsystems Engineering at the University of Freiburg (both in 2014) and various shorter academic visiting appointments, e.g., at UC Berkeley during Winter 2017 and Imperial College London during Summer 2018.
Boris Houska has been recipient of awards including ICCOPT Best Paper Prize for a Young Researcher in Continuous Optimization (Finalist, Top 3), a Marie-Curie Fellowship for the project Next Generation Algorithms for Robust and Global Optimization of Dynamic Systems, as well as an ShanghaiTech Excellent Professor Award from ShanghaiTech University. His paper on “ACADO Toolkit — An open source framework for automatic control and dynamic optimization” has been listed as highly cited paper by Web of Science.
Selected References:
• M.E. Villanueva, M.A. Müller, B. Houska. Configuration-Constrained Tube MPC.
Automatica, 2023. (provision. accepted, https://arxiv.org/abs/2208.12554)
• B. Houska, M.E. Villanueva. Robust optimization for MPC. Handbook of MPC,
pages 423–454, Springer, 2019
• M.E. Villanueva, E. De Lazzari, M.A. Müller, B. Houska. A set-theoretic
generalization of dissipativity with applications in Tube MPC. Automatica, 2020
• F. Wu, M.E. Villanueva, B. Houska. Ambiguity Tube MPC. Automatica, 2022
Set-based computing methods are the foundation of validated arithmetics, reliable computing, uncertainty propagation, computational configuration analysis, optimization of control invariant sets, robust control, hybrid system analysis, safe learning and dual control, multi-objective optimization, ambiguity analysis, and many others. Therefore, the first part of this talk is about recent breakthroughs in the field of set-based computing. In detail, we focus on a novel class of configuration-constrained polytopes that admit a joint affine parameterization of their vertices and facets. This parameterization can be used to optimize the location and geometric shapes of potentially high dimensional polytopes with millions of facets and vertices by relying on a large-scale linear program (LP) solver. And the second part of this talk discusses applications of such configuration-constrained polytopes in the context of robust model predictive control, set-theoretic dissipativity theory, information-theoretic safe learning, as well as polytopic dual control. The talk concludes with a general assessment of the state of the art in set-based computing and highlights important avenues for future research.
Bio:
Boris Houska is an associate professor at the School of Information Science and Technology at ShanghaiTech University. His research interests include numerical optimization and optimal control, robust and global optimization, as well as fast model predictive control algorithms.
Boris Houska received a diploma in mathematics and physics from the University of Heidelberg in 2007, and a Ph.D. in Electrical Engineering from KU Leuven in 2011. From 2012 to 2013 he was a postdoctoral researcher at the Centre for Process Systems Engineering at Imperial College London. Subsequently, from 2013-14, he has worked as a research faculty member at Shanghai Jiao Tong University. Moreover, he has held visiting professor positions at the Freiburg Institute for Advanced Studies as well as at the Institute for Microsystems Engineering at the University of Freiburg (both in 2014) and various shorter academic visiting appointments, e.g., at UC Berkeley during Winter 2017 and Imperial College London during Summer 2018.
Boris Houska has been recipient of awards including ICCOPT Best Paper Prize for a Young Researcher in Continuous Optimization (Finalist, Top 3), a Marie-Curie Fellowship for the project Next Generation Algorithms for Robust and Global Optimization of Dynamic Systems, as well as an ShanghaiTech Excellent Professor Award from ShanghaiTech University. His paper on “ACADO Toolkit — An open source framework for automatic control and dynamic optimization” has been listed as highly cited paper by Web of Science.
Selected References:
• M.E. Villanueva, M.A. Müller, B. Houska. Configuration-Constrained Tube MPC.
Automatica, 2023. (provision. accepted, https://arxiv.org/abs/2208.12554)
• B. Houska, M.E. Villanueva. Robust optimization for MPC. Handbook of MPC,
pages 423–454, Springer, 2019
• M.E. Villanueva, E. De Lazzari, M.A. Müller, B. Houska. A set-theoretic
generalization of dissipativity with applications in Tube MPC. Automatica, 2020
• F. Wu, M.E. Villanueva, B. Houska. Ambiguity Tube MPC. Automatica, 2022
Practical information
- General public
- Free