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math.OC2026

Structure, Analysis, and Synthesis of First-Order Algorithms

Jared Miller, Carsten Scherer, Fabian Jakob +1

Optimization algorithms can be interpreted through the lens of dynamical systems as the interconnection of linear systems and a set of subgradient nonlinearities. This dynamical sy…

math.OC2026

Analysis and Synthesis of Switched Optimization Algorithms

Jared Miller, Fabian Jakob, Carsten Scherer +1

Deployment of optimization algorithms over communication networks face challenges associated with time delays and corruptions. Fixed time delays can destabilize popular gradient-ba…

math.OC2026

A Linear Parameter-Varying Framework for the Analysis of Time-Varying Optimization Algorithms

Fabian Jakob, Andrea Iannelli

In this paper we propose a framework to analyze iterative first-order optimization algorithms for time-varying convex optimization. We assume that the temporal variability is cause…

math.OC2025

Accelerated ADMM: Automated Parameter Tuning and Improved Linear Convergence

Meisam Tavakoli, Fabian Jakob, Guido Carnevale +2

This work studies the linear convergence of an accelerated scheme of the Alternating Direction Method of Multipliers (ADMM) for strongly convex and Lipschitz-smooth problems. We us…

math.OC2025

Online Convex Optimization and Integral Quadratic Constraints: An automated approach to regret analysis

Fabian Jakob, Andrea Iannelli

We propose a novel approach for analyzing dynamic regret of first-order constrained online convex optimization algorithms for strongly convex and Lipschitz-smooth objectives. Cruci…

math.OC2025

Complexity guarantees for risk-neutral generalized Nash equilibrium problems

Haochen Tao, Andrea Iannelli, Meggie Marschner +3

In this paper, we address \ac{SGNEP} seeking with risk-neutral agents. Our main contribution lies the development of a stochastic variance-reduced gradient (SVRG) technique, modifi…