2 citations · 6 across the 21 of their papers we have counts for
9 papers · 1 filter
Improved Complexity for Smooth Nonconvex Optimization: A Two-Level Online Learning Approach with Quasi-Newton Methods
Ruichen Jiang, Aryan Mokhtari, Francisco Patitucci
We study the problem of finding an -first-order stationary point (FOSP) of a smooth function, given access only to gradient information. The best-known gradient query complexity…
Online Learning Guided Quasi-Newton Methods with Global Non-Asymptotic Convergence
Ruichen Jiang, Aryan Mokhtari
In this paper, we propose a quasi-Newton method for solving smooth and monotone nonlinear equations, including unconstrained minimization and minimax optimization as special cases.…
Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
Ruichen Jiang, Devyani Maladkar, Aryan Mokhtari
Adaptive gradient methods, such as AdaGrad, are among the most successful optimization algorithms for neural network training. While these methods are known to achieve better dimen…
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
Ruichen Jiang, Ali Kavis, Qiujiang Jin +2
We propose adaptive, line search-free second-order methods with optimal rate of convergence for solving convex-concave min-max problems. By means of an adaptive step size, our algo…
Stochastic Newton Proximal Extragradient Method
Ruichen Jiang, Michał Dereziński, Aryan Mokhtari
Stochastic second-order methods achieve fast local convergence in strongly convex optimization by using noisy Hessian estimates to precondition the gradient. However, these methods…
Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search
Qiujiang Jin, Ruichen Jiang, Aryan Mokhtari
In this paper, we present the first explicit and non-asymptotic global convergence rates of the BFGS method when implemented with an inexact line search scheme satisfying the Armij…