1 code implementation • 28 Mar 2024 • Sayak Mukherjee, Andrea Simonetto, Hadi Jamali-Rad
Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature.
no code implementations • 7 Dec 2023 • Eliabelle Mauduit, Andrea Simonetto
Motivated by extracting and summarizing relevant information in short sentence settings, such as satisfaction questionnaires, hotel reviews, and X/Twitter, we study the problem of clustering words in a hierarchical fashion.
2 code implementations • 3 May 2023 • Elisabetta Perotti, Ana M. Ospina, Gianluca Bianchin, Andrea Simonetto, Emiliano Dall'Anese
We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company.
no code implementations • 13 Mar 2023 • Filippo Fabiani, Andrea Simonetto
Modern socio-technical systems typically consist of many interconnected users and competing service providers, where notions like market equilibrium are tightly connected to the ``evolution'' of the network of users.
no code implementations • 24 Jun 2022 • Andrea Simonetto, Ivano Notarnicola
In this paper, we investigate how to allocate limited resources to {locally interacting} communities in a way to maximize a pertinent notion of equitability.
no code implementations • 24 Mar 2022 • Filippo Fabiani, Andrea Simonetto, Paul J. Goulart
We investigate both stationary and time-varying, nonmonotone generalized Nash equilibrium problems that exhibit symmetric interactions among the agents, which are known to be potential.
no code implementations • 6 Nov 2021 • Filippo Fabiani, Andrea Simonetto, Paul J. Goulart
We consider quadratic, nonmonotone generalized Nash equilibrium problems with symmetric interactions among the agents.
1 code implementation • 27 May 2021 • Nicola Bastianello, Andrea Simonetto, Emiliano Dall'Anese
This paper presents a new regularization approach -- termed OpReg-Boost -- to boost the convergence and lessen the asymptotic error of online optimization and learning algorithms.
no code implementations • 10 Aug 2020 • Ivano Notarnicola, Andrea Simonetto, Francesco Farina, Giuseppe Notarstefano
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 specific user.
no code implementations • 24 Apr 2020 • 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 data.
no code implementations • 7 Jan 2020 • 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 constraints via quantum heuristic approaches.
Combinatorial Optimization Quantum Physics Optimization and Control
no code implementations • 17 Oct 2019 • Emiliano Dall'Anese, Andrea Simonetto, Stephen Becker, Liam Madden
Approaches for the design of time-varying or online first-order optimization methods are discussed, with emphasis on algorithms that can handle errors in the gradient, as may arise when the gradient is estimated.
no code implementations • 4 Oct 2019 • Amirhossein Ajalloeian, Andrea Simonetto, Emiliano Dall'Anese
The online proximal-gradient method is inexact, in the sense that: (i) it relies on an approximate first-order information of the smooth component of the cost; and, (ii) the proximal operator (with respect to the non-smooth term) may be computed only up to a certain precision.
1 code implementation • 10 Sep 2018 • Albert Akhriev, Jakub Marecek, Andrea Simonetto
In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformly-distributed measurement noise and arbitrarily-distributed ``sparse'' noise.
no code implementations • 14 Feb 2016 • Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus
We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation.