3 papers
math.OC2026
Mirror Polyak and a Primal-Dual Lifting
Frederik Kunstner, Ryan D'Orazio, Victor S. Portella +1
First-order methods typically require a specific step-size that depends on the regularity conditions of the objective function, such as the smoothness, Lipschitz continuity, or str…
math.OC2026
New Perspectives on the Polyak Stepsize: Surrogate Functions and Negative Results
Francesco Orabona, Ryan D'Orazio
The Polyak stepsize has been proven to be a fundamental stepsize in convex optimization, giving near optimal gradient descent rates across a wide range of assumptions. The universa…
cs.LG2025
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
Ryan D'Orazio, Danilo Vucetic, Zichu Liu +3
Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minimizing projected Bellman error and min-ma…