Online scaled gradient methods adapt matrix step sizes via online learning, match the best fixed step size asymptotically, and achieve non-asymptotic superlinear convergence on smooth strongly convex problems.
Acceleration by stepsize hedging: Silver stepsize schedule for smooth convex optimization.Mathematical Programming, pages 1–14, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
math.OC 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Gradient Methods with Online Scaling Part I. Theoretical Foundations
Online scaled gradient methods adapt matrix step sizes via online learning, match the best fixed step size asymptotically, and achieve non-asymptotic superlinear convergence on smooth strongly convex problems.