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math.OC2026

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…

math.OC2026

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.…

math.OC2025

Extending Douglas-Rachford Splitting for Convex Optimization

Max Nilsson, Anton à kerman, Pontus Giselsson

The Douglas-Rachford splitting method is a classical and widely used algorithm for solving monotone inclusions involving the sum of two maximally monotone operators. It was recentl…

math.OC2025

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…

math.OC2025

Automated tight Lyapunov analysis for first-order methods

Manu Upadhyaya, Sebastian Banert, Adrien B. Taylor +1

We present a methodology for establishing the existence of quadratic Lyapunov inequalities for a wide range of first-order methods used to solve convex optimization problems. In pa…

math.OC2025

The Symmetry Coefficient of Positively Homogeneous Functions

Max Nilsson, Pontus Giselsson

The Bregman distance is a central tool in convex optimization, particularly in first-order gradient descent and proximal-based algorithms. Such methods enable optimization of funct…