366 citations · 2k across the 43 of their papers we have counts for
52 papers
High-Resolution Inertial Dynamics with Time-Rescaled Gradients for Nonsmooth Convex Optimization
Manh Hung Le, Andrea Simonetto
We study nonsmooth convex minimization through a continuous-time dynamical system that can be seen as a high-resolution ODE of Nesterov Accelerated Gradient (NAG) adapted to the no…
Optimizing Variational Circuits for Higher-Order Binary Optimization
Zoé Verchère, Sourour Elloumi, Andrea Simonetto
Variational quantum algorithms have been advocated as promising candidates to solve combinatorial optimization problems on near-term quantum computers. Their methodology involves t…
Incentives and co-evolution: Steering linear dynamical systems with noncooperative agents
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 ``…
Nonlinear Optimization Filters for Stochastic Time-Varying Convex Optimization
Andrea Simonetto, Paolo Massioni
We look at a stochastic time-varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the in…
Optimal Power Flow Pursuit in the Alternating Current Model
Jie Liu, Antonio Bellon, Andrea Simonetto +2
Transmission-constrained problems in power systems can be cast as polynomial optimization problems whose coefficients vary over time. We consider the complications therein and sugg…
Achievement and Fragility of Long-term Equitability
Andrea Simonetto, Ivano Notarnicola
Equipping current decision-making tools with notions of fairness, equitability, or other ethically motivated outcomes, is one of the top priorities in recent research efforts in ma…
Sketching the Best Approximate Quantum Compiling Problem
Liam Madden, Albert Akhriev, Andrea Simonetto
This paper considers the problem of quantum compilation from an optimization perspective by fixing a circuit structure of CNOTs and rotation gates then optimizing over the rotation…
Personalized incentives as feedback design in generalized Nash equilibrium problems
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 poten…
A Quantum Algorithm for the Sub-Graph Isomorphism Problem
Nicola Mariella, Andrea Simonetto
We propose a novel variational method for solving the sub-graph isomorphism problem on a gate-based quantum computer. The method relies (1) on a new representation of the adjacency…
Learning equilibria with personalized incentives in a class of nonmonotone games
Filippo Fabiani, Andrea Simonetto, Paul J. Goulart
We consider quadratic, nonmonotone generalized Nash equilibrium problems with symmetric interactions among the agents. Albeit this class of games is known to admit a potential func…
Best Approximate Quantum Compiling Problems
Liam Madden, Andrea Simonetto
We study the problem of finding the best approximate circuit that is the closest (in some pertinent metric) to a target circuit, and which satisfies a number of hardware constraint…
OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression
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.…
Time-Varying Optimization of Networked Systems with Human Preferences
Ana M. Ospina, Andrea Simonetto, Emiliano Dall'Anese
This paper considers a time-varying optimization problem associated with a network of systems, with each of the systems shared by (and affecting) a number of individuals. The objec…
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…
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…
Optimization and Learning with Information Streams: Time-varying Algorithms and Applications
Emiliano Dall'Anese, Andrea Simonetto, Stephen Becker +1
There is a growing cross-disciplinary effort in the broad domain of optimization and learning with streams of data, applied to settings where traditional batch optimization techniq…
Inexact Online Proximal-gradient Method for Time-varying Convex Optimization
Amirhossein Ajalloeian, Andrea Simonetto, Emiliano Dall'Anese
This paper considers an online proximal-gradient method to track the minimizers of a composite convex function that may continuously evolve over time. The online proximal-gradient…