49 citations · 87 across the 17 of their papers we have counts for
17 papers
Learning-rate-free Momentum SGD with Reshuffling Converges in Nonsmooth Nonconvex Optimization
Xiaoyin Hu, Nachuan Xiao, Xin Liu +1
In this paper, we propose a generalized framework for developing learning-rate-free momentum stochastic gradient descent (SGD) methods in the minimization of nonsmooth nonconvex fu…
Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization
Nachuan Xiao, Kuangyu Ding, Xiaoyin Hu +1
In this paper, we consider the minimization of a nonsmooth nonconvex objective function over a closed convex subset of , with additional nonsmoot…
Wasserstein distributionally robust optimization and its tractable regularization formulations
Hong T. M. Chu, Meixia Lin, Kim-Chuan Toh
We study a variety of Wasserstein distributionally robust optimization (WDRO) problems where the distributions in the ambiguity set are chosen by constraining their Wasserstein dis…
Adam-family Methods with Decoupled Weight Decay in Deep Learning
Kuangyu Ding, Nachuan Xiao, Kim-Chuan Toh
In this paper, we investigate the convergence properties of a wide class of Adam-family methods for minimizing quadratically regularized nonsmooth nonconvex optimization problems,…
Self-adaptive ADMM for semi-strongly convex problems
Tianyun Tang, Kim-Chuan Toh
In this paper, we develop a self-adaptive ADMM that updates the penalty parameter adaptively. When one part of the objective function is strongly convex i.e., the problem is semi-s…
Quantifying low rank approximations of third order symmetric tensors
Shenglong Hu, Defeng Sun, Kim-Chuan Toh
In this paper, we present a method to certify the approximation quality of a low rank tensor to a given third order symmetric tensor. Under mild assumptions, best low rank approxim…