1 citations · 2 across the 6 of their papers we have counts for
6 papers
Finding Simple Proofs for First-Order Optimization
Daniel Berg Thomsen, Manu Upadhyaya, Baptiste Goujaud +2
Progress in mathematics often requires more than a certificate of truth: it requires proof structures that are transparent, checkable, and reusable. Automated systems can increasin…
An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit
Manu Upadhyaya, Daniel Berg Thomsen, Aymeric Dieuleveut +1
We introduce Prox-ITEM, an optimal proximal gradient method for minimizing , where is smooth and strongly convex, and is convex, proper, and lower semicontinuous. In t…
The Chambolle-Pock method converges weakly with and
Manu Upadhyaya
The Chambolle-Pock method, also known as the primal-dual hybrid gradient method, is a standard first-order algorithm for convex-concave saddle-point problems and composite convex o…
The AutoLyap software suite for computer-assisted Lyapunov analyses of first-order methods
Manu Upadhyaya, Shuvomoy Das Gupta, Adrien B. Taylor +2
We introduce AutoLyap, a software suite that assists with Lyapunov analyses of a wide class of first-order methods for structured optimization and inclusion problems. Lyapunov anal…
A Lyapunov analysis of Korpelevich's extragradient method with fast and flexible extensions
Manu Upadhyaya, Puya Latafat, Pontus Giselsson
We develop a Lyapunov-based analysis of Korpelevich's extragradient method and show that it achieves an last-iterate convergence rate of the constructed Lyapunov function.…
The Chambolle--Pock method converges weakly with and
Sebastian Banert, Manu Upadhyaya, Pontus Giselsson
The Chambolle--Pock method is a versatile three-parameter algorithm designed to solve a broad class of composite convex optimization problems, which encompass two proper, lower sem…