M
Maria Prandini
Researcher at Polytechnic University of Milan
Publications - 219
Citations - 4710
Maria Prandini is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Probabilistic logic & Optimization problem. The author has an hindex of 29, co-authored 212 publications receiving 4032 citations. Previous affiliations of Maria Prandini include University of Oxford & Brescia University.
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A decentralized approach to multi-agent MILPs: Finite-time feasibility and performance guarantees
TL;DR: The proposed approach is inspired by a recent method to the MILP approximate solution via dual decomposition and constraint tightening, and presents the advantage of guaranteeing feasibility in finite-time and providing better performance guarantees.
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Policy Search for the Optimal Control of Markov Decision Processes: A Novel Particle-Based Iterative Scheme
TL;DR: An iterative policy building scheme that adds new particles to improve the policy performance and is also capable of removing redundant particles is introduced, demonstrating the scalability of the proposed approach as the dimensionality of the state-space grows.
Journal ArticleDOI
Model reduction of switched affine systems
TL;DR: A stochastic setting is considered and a randomized method for the selection of the reduced order is proposed and the performance of the proposed approach is illustrated through a multi-room temperature control example.
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Position paper on the challenges posed by modern applications to cyber-physical systems theory
Frank Allgöwer,João Borges de Sousa,James Kapinski,Pieter J. Mosterman,Jens Oehlerking,Patrick Panciatici,Maria Prandini,Akshay Rajhans,Paulo Tabuada,Philipp Wenzelburger +9 more
TL;DR: The goal of this position paper is to provide the cyber-physical systems community, and especially young researchers, a clear view on what are research directions worth pursuing motivated by the challenges posed by modern applications.
Proceedings ArticleDOI
Stochastic constrained control: Trading performance for state constraint feasibility
TL;DR: A control design methodology is proposed where the appropriate trade-off between the minimization of the control cost and the satisfaction of the state constraints can be decided by introducing appropriate chance-constrained problems depending on some parameter to be tuned.