activity
20242026
most citedUniversal subgradient and proximal bundle methods for convex and strongly convex hybrid composite optimization

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing 2026Show all

2 papers · 1 filter

math.OC2026

Accuracy Certificates for Convex Optimization at Accelerated Rates via Primal-Dual Averaging

Matthew X. Burns, Jiaming Liang

Many works in convex optimization provide rates for achieving a small primal gap. However, this quantity is typically unavailable in practice. In this work, we show that solving a…

math.OC2026

Improved Analysis of Restarted Accelerated Gradient and Augmented Lagrangian Methods via Inexact Proximal Point Frameworks

Matthew X. Burns, Jiaming Liang

This paper studies a class of double-loop (inner-outer) algorithms for convex composite optimization. For unconstrained problems, we develop a restarted accelerated composite gradi…