15 papers
Distributed Personalized Gradient Tracking with Convex Parametric Models
Ivano Notarnicola, Andrea Simonetto, Francesco Farina +1
We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a…
Personalized Demand Response via Shape-Constrained Online Learning
Ana M. Ospina, Andrea Simonetto, Emiliano Dall'Anese
This paper formalizes a demand response task as an optimization problem featuring a known time-varying engineering cost and an unknown (dis)comfort function. Based on this model, t…
Quantum Computing for Finance: State of the Art and Future Prospects
Daniel J. Egger, Claudio Gambella, Jakub Marecek +6
This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to qua…
Smooth Strongly Convex Regression
Andrea Simonetto
Convex regression (CR) is the problem of fitting a convex function to a finite number of noisy observations of an underlying convex function. CR is important in many domains and on…
Multi-block ADMM Heuristics for Mixed-Binary Optimization on Classical and Quantum Computers
Claudio Gambella, Andrea Simonetto
Solving combinatorial optimization problems on current noisy quantum devices is currently being advocated for (and restricted to) binary polynomial optimization with equality const…
A city-scale IoT-enabled ridesharing platform
Claudio Gambella, Julien Monteil, Anton Dekusar +3
The advent of on-demand mobility systems is expected to have a tremendous potential on the wellness of transportation users in cities. Yet such positive effects are reached when th…