activity
20152026
most citedBregman Proximal Framework for Deep Linear Neural Networks

5 citations · 9 across the 19 of their papers we have counts for

collaborators

22 papers

math.OC2026

On the Regularity of Generalized Conjugate Functions

Konstantinos Oikonomidis, Emanuel Laude, Panagiotis Patrinos

We investigate regularity properties of generalized conjugate functions induced by a general coupling function and the associated generalized proximal mapping. Our main results pro…

math.OC2026

All roads lead to Rome: Path-following Augmented Lagrangian Methods via Bregman Proximal Regularization

Emanuel Laude

We study Bregman proximal augmented Lagrangian methods with second-order oracles for convex convex-composite optimization problems. The outer loop is an instance of the Bregman pro…

physics.plasm-ph2025

Optimizing Particle Transport for Enhanced Confinement in Quasi-Isodynamic Stellarators

A. Bañón Navarro, A. Di Siena, F. Jenko +2

Despite significant advances in reducing turbulent heat losses, modern quasi-isodynamic (QI) stellarators -- such as Stellaris -- continue to suffer from poor particle confinement,…

cs.LG2025

ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks

Santiago A. Cadena, Andrea Merlo, Emanuel Laude +8

Stellarators are magnetic confinement devices under active development to deliver steady-state carbon-free fusion energy. Their design involves a high-dimensional, constrained opti…

math.OC2025

Forward-backward splitting under the light of generalized convexity

Konstantinos Oikonomidis, Emanuel Laude, Panagiotis Patrinos

In this paper we present a unifying framework for continuous optimization methods grounded in the concept of generalized convexity. Utilizing the powerful theory of -convexity,…

math.OC2025

Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness

Konstantinos Oikonomidis, Jan Quan, Emanuel Laude +1

We analyze nonlinearly preconditioned gradient methods for solving smooth minimization problems. We introduce a generalized smoothness property, based on the notion of abstract con…