3 citations · 3 across the 5 of their papers we have counts for
8 papers · 1 filter
Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp
Wenzhi Gao, Zhaonan Qu, Yinyu Ye +2
We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem. Despite extensive literature on the global convergence of SK and its variants, its local linear converg…
New Results on the Polyak Stepsize: Tight Convergence Analysis and Universal Function Classes
Chang He, Wenzhi Gao, Bo Jiang +2
In this paper, we revisit a classical adaptive stepsize strategy for gradient descent: the Polyak stepsize (PolyakGD), originally proposed in Polyak (1969). We study the convergenc…
Gradient Methods with Online Scaling Part II. Practical Aspects
Ya-Chi Chu, Wenzhi Gao, Yinyu Ye +1
Part I of this work [Gao25] establishes online scaled gradient methods (OSGM), a framework that utilizes online convex optimization to adapt stepsizes in gradient methods. This pap…
Gradient Methods with Online Scaling Part I. Theoretical Foundations
Wenzhi Gao, Ya-Chi Chu, Yinyu Ye +1
This paper establishes the theoretical foundations of the online scaled gradient methods (OSGM), a framework that utilizes online learning to adapt stepsizes and provably accelerat…
Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent
Ya-Chi Chu, Wenzhi Gao, Yinyu Ye +1
This paper investigates the convergence properties of the hypergradient descent method (HDM), a 25-year-old heuristic originally proposed for adaptive stepsize selection in stochas…
Randomized Nyström Preconditioned Interior Point-Proximal Method of Multipliers
Ya-Chi Chu, Luiz-Rafael Santos, Madeleine Udell
We present a new algorithm for convex separable quadratic programming (QP) called Nys-IP-PMM, a regularized interior-point solver that uses low-rank structure to accelerate solutio…