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Main Authors: Oikonomidis, Konstantinos, Bodard, Alexander, Quan, Jan, Patrinos, Panagiotis
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2511.20370
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author Oikonomidis, Konstantinos
Bodard, Alexander
Quan, Jan
Patrinos, Panagiotis
author_facet Oikonomidis, Konstantinos
Bodard, Alexander
Quan, Jan
Patrinos, Panagiotis
contents We study a continuous-time dynamical system which arises as the limit of a broad class of nonlinearly preconditioned gradient methods. Under mild assumptions, we establish existence of global solutions and derive Lyapunov-based convergence guarantees. For convex costs, we prove a sublinear decay in a geometry induced by some reference function, and under a generalized gradient-dominance condition we obtain exponential convergence. We further uncover a duality connection with mirror descent, and use it to establish that the flow of interest solves an infinite-horizon optimal-control problem of which the value function is the Bregman divergence generated by the cost. These results clarify the structure and optimization behavior of nonlinearly preconditioned gradient flows and connect them to known continuous-time models in non-Euclidean optimization.
format Preprint
id arxiv_https___arxiv_org_abs_2511_20370
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Nonlinearly preconditioned gradient flows
Oikonomidis, Konstantinos
Bodard, Alexander
Quan, Jan
Patrinos, Panagiotis
Optimization and Control
Dynamical Systems
We study a continuous-time dynamical system which arises as the limit of a broad class of nonlinearly preconditioned gradient methods. Under mild assumptions, we establish existence of global solutions and derive Lyapunov-based convergence guarantees. For convex costs, we prove a sublinear decay in a geometry induced by some reference function, and under a generalized gradient-dominance condition we obtain exponential convergence. We further uncover a duality connection with mirror descent, and use it to establish that the flow of interest solves an infinite-horizon optimal-control problem of which the value function is the Bregman divergence generated by the cost. These results clarify the structure and optimization behavior of nonlinearly preconditioned gradient flows and connect them to known continuous-time models in non-Euclidean optimization.
title Nonlinearly preconditioned gradient flows
topic Optimization and Control
Dynamical Systems
url https://arxiv.org/abs/2511.20370