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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Dual decomposition for multi-agent distributed optimization with coupling constraints
TL;DR: In this paper, a distributed algorithm is proposed to minimize the sum of the agents' objective functions subject to both local and coupling constraints, where dual decomposition and proximal minimization are combined in an iterative scheme.
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A randomized algorithm for nonlinear model structure selection
TL;DR: The MSS problem is here recast into a probabilistic framework based on which a randomized algorithm for MSS is derived, denoted RaMSS, which introduces a tentative probability distribution over models and progressively updates it by extracting useful information on the importance of each term from sampled model structures.
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Optimal Coordinated Motions of Multiple Agents Moving on a Plane
TL;DR: This work addresses the problem of optimal coordinated motions of multiple agents moving in the same planar region and proposes a convex optimization algorithm to compute within each homotopy type a solution to the optimization problem restricted to the class of multilegged maneuvers.
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Tracking-ADMM for Distributed Constraint-Coupled Optimization
TL;DR: In this paper, a dynamic average consensus protocol was proposed to design a fully distributed scheme for constraint-coupled optimization problems in which agents of a network aim to cooperatively minimize the sum of local objective functions subject to individual constraints and a common linear coupling constraint.
Proceedings ArticleDOI
Stochastic unit commitment and reserve scheduling: A tractable formulation with probabilistic certificates
Kostas Margellos,Vahab Rostampour,Maria Vrakopoulou,Maria Prandini,Göran Andersson,John Lygeros +5 more
TL;DR: A unified framework to deal with stochastic variants of the standard unit commitment and reserve scheduling problems for systems with uncertain generation while providing a-priori probabilistic certificates for the robustness properties of the resulting solution is provided.