42 citations · 163 across the 36 of their papers we have counts for
6 papers · 1 filter
Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations
Haishan Ye, Dachao Lin, Zhihua Zhang
In this paper, we propose the greedy and random Broyden's method for solving nonlinear equations. Specifically, the greedy method greedily selects the direction to maximize a certa…
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
Rui Pan, Haishan Ye, Tong Zhang
Learning rate schedulers have been widely adopted in training deep neural networks. Despite their practical importance, there is a discrepancy between its practice and its theoreti…
Explicit Superlinear Convergence Rates of Broyden's Methods in Nonlinear Equations
Dachao Lin, Haishan Ye, Zhihua Zhang
In this paper, we study the explicit superlinear convergence rates of quasi-Newton methods. We particularly focus on the classical Broyden's method for solving nonlinear equations.…
Explicit Superlinear Convergence Rates of The SR1 Algorithm
Haishan Ye, Dachao Lin, Zhihua Zhang +1
We study the convergence rate of the famous Symmetric Rank-1 (SR1) algorithm which has wide applications in different scenarios. Although it has been extensively investigated, SR1…
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods
Dachao Lin, Haishan Ye, Zhihua Zhang
Optimization is important in machine learning problems, and quasi-Newton methods have a reputation as the most efficient numerical schemes for smooth unconstrained optimization. In…
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate
Haishan Ye, Tong Zhang
Due to the rapid growth of smart agents such as weakly connected computational nodes and sensors, developing decentralized algorithms that can perform computations on local agents…