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math.OC2026
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…
math.OC2026
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…
math.OC2025
Complexity of Minimizing Regularized Convex Quadratic Functions
Daniel Berg Thomsen, Nikita Doikov
In this work, we study the iteration complexity of gradient methods for minimizing convex quadratic functions regularized by powers of Euclidean norms. We show that, due to the uni…