activity
20182026
most citedDistributed generalized Nash equilibrium seeking in aggregative games on time-varying networks

149 citations · 416 across the 31 of their papers we have counts for

collaborators
Showing 2026Show all

6 papers · 1 filter

eess.SY2026

The Price of Distributional Robustness in Linear Quadratic Control

Andrea Martin, Giuseppe Belgioioso

Distributionally robust (DR) optimization seeks decisions that perform best under the most adverse law within a given ambiguity set, enabling the design of data-driven controllers…

eess.SY2026

Finite-sample guarantees for data-driven operator splitting methods via martingale inequalities

Andrea Martin, Filippo Fabiani, Giuseppe Belgioioso

Operator splitting methods are a fundamental class of algorithms for solving structured monotone inclusion problems arising in optimization, control, and game theory. We consider t…

cs.GT2026

Distributed Algorithm for Robust Wardrop Equilibrium in Uncertain Aggregative Congestion Games

Huan Peng, Guanpu Chen, Giuseppe Belgioioso +1

This paper considers a class of aggregative congestion games with uncertain coupling constraints, and devises a distributed algorithm to seek the robust generalized Wardrop equilib…

eess.SY2026

Adaptive Tuning of Online Feedback Optimization for Process Control Applications

Marta Zagorowska, Lukas Ortmann, Giuseppe Belgioioso +1

Online Feedback Optimization leverages properties of optimization algorithms to develop controllers for systems with limited model availability, which is often the case in process…

eess.SY2026

DR-DAQP: A Hybrid Operator Splitting and Active-Set Solver for Affine Variational Inequalities

Daniel Arnström, Emilio Benenati, Giuseppe Belgioioso

We present DR-DAQP, an open-source solver for strongly monotone affine variational inequalities (AVIs) that combines Douglas-Rachford operator splitting with an active-set accelera…

eess.SY2026

Learning to accelerate Krasnosel'skii-Mann fixed-point iterations with guarantees

Andrea Martin, Giuseppe Belgioioso

We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our idea is to deliberately inject summable…