82 citations · 546 across the 65 of their papers we have counts for
6 papers · 2 filters
Cubic Regularization with Momentum for Nonconvex Optimization
Zhe Wang, Yi Zhou, Yingbin Liang +1
Momentum is a popular technique to accelerate the convergence in practical training, and its impact on convergence guarantee has been well-studied for first-order algorithms. Howev…
Asynchronous decentralized accelerated stochastic gradient descent
Guanghui Lan, Yi Zhou
In this work, we introduce an asynchronous decentralized accelerated stochastic gradient descent type of method for decentralized stochastic optimization, considering communication…
A Note on Inexact Condition for Cubic Regularized Newton's Method
Zhe Wang, Yi Zhou, Yingbin Liang +1
This note considers the inexact cubic-regularized Newton's method (CR), which has been shown in \cite{Cartis2011a} to achieve the same order-level convergence rate to a secondary s…
Accelerated Stochastic Algorithms for Nonconvex Finite-sum and Multi-block Optimization
Guanghui Lan, Yu Yang
In this paper, we present new stochastic methods for solving two important classes of nonconvex optimization problems. We first introduce a randomized accelerated proximal gradient…
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization
Zhe Wang, Yi Zhou, Yingbin Liang +1
Cubic regularization (CR) is an optimization method with emerging popularity due to its capability to escape saddle points and converge to second-order stationary solutions for non…
Robust affine control of linear stochastic systems
Georgios Kotsalis, Guanghui Lan
In this work we provide a computationally tractable procedure for designing affine control policies, applied to constrained, discrete-time, partially observable, linear systems sub…