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Bartolomeo Stellato
Researcher at Princeton University
Publications - 49
Citations - 1660
Bartolomeo Stellato is an academic researcher from Princeton University. The author has contributed to research in topics: Optimization problem & Solver. The author has an hindex of 17, co-authored 40 publications receiving 891 citations. Previous affiliations of Bartolomeo Stellato include University of Oxford & Stanford University.
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Journal ArticleDOI
OSQP: An Operator Splitting Solver for Quadratic Programs
TL;DR: This work presents 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.
Journal ArticleDOI
OSQP: an operator splitting solver for quadratic programs
TL;DR: OSQP as discussed by the authors is 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.
Journal ArticleDOI
High-Speed Finite Control Set Model Predictive Control for Power Electronics
TL;DR: To the authors’ knowledge, this is the first time direct MPC for current control has been implemented on an FPGA solving the integer optimization problem in real time and achieving comparable performance to formulations with long prediction horizons.
Journal ArticleDOI
COVID-19 mortality risk assessment: An international multi-center study.
Dimitris Bertsimas,Galit Lukin,Luca Mingardi,Omid Nohadani,Agni Orfanoudaki,Bartolomeo Stellato,Holly Wiberg,Sara González-García,Carlos Luis Parra-Calderón,Ken Robinson,Michelle Schneider,Barry Stein,Alberto Estirado,Lia a Beccara,Rosario Canino,Martina Dal Bello,Federica Pezzetti,Angelo Pan +17 more
TL;DR: The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features and is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.
Proceedings ArticleDOI
OSQP: An Operator Splitting Solver for Quadratic Programs
TL;DR: OSQP as mentioned in this paper is a general purpose solver for 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 in each iteration.