Manu Upadhyaya

activity

20232026

most citedA Lyapunov analysis of Korpelevich's extragradient method with fast and flexible extensions

1 citations · 2 across the 6 of their papers we have counts for

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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 $f + g$ , where $f$ is smooth and strongly convex, and $g$ is convex, proper, and lower semicontinuous. In t…

math.OC2026

The Chambolle-Pock method converges weakly with $0 < θ \leq 1/2$ and $τ σ ∥ L ∥^{2} < 4 θ (2 - θ) / (1 - 2 θ + 9 θ^{2} - 4 θ^{3})$

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…

Finding Simple Proofs for First-Order Optimization

An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit

The Chambolle-Pock method converges weakly with 0<θ≤1/2 and τσ∥L∥2<4θ(2−θ)/(1−2θ+9θ2−4θ3)

The Chambolle-Pock method converges weakly with $0 < θ \leq 1/2$ and $τ σ ∥ L ∥^{2} < 4 θ (2 - θ) / (1 - 2 θ + 9 θ^{2} - 4 θ^{3})$