366 citations · 2k across the 43 of their papers we have counts for
8 papers · 1 filter
Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization
Nicola Bastianello, Andrea Simonetto, Ruggero Carli
This paper introduces a dual-regularized ADMM approach to distributed, time-varying optimization. The proposed algorithm is designed in a prediction-correction framework, in which…
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…
Time-Varying Convex Optimization: Time-Structured Algorithms and Applications
Andrea Simonetto, Emiliano Dall'Anese, Santiago Paternain +2
Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms g…
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…
Extrapolation-based Prediction-Correction Methods for Time-varying Convex Optimization
Nicola Bastianello, Ruggero Carli, Andrea Simonetto
In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of…
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…