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Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization
Xufeng Cai, Chaobing Song, Stephen J. Wright +1
Nonconvex optimization is central in solving many machine learning problems, in which block-wise structure is commonly encountered. In this work, we propose cyclic block coordinate…
On the Complexity of a Practical Primal-Dual Coordinate Method
Ahmet Alacaoglu, Volkan Cevher, Stephen J. Wright
We prove complexity bounds for the primal-dual algorithm with random extrapolation and coordinate descent (PURE-CD), which has been shown to obtain good practical performance for s…
Coordinate Linear Variance Reduction for Generalized Linear Programming
Chaobing Song, Cheuk Yin Lin, Stephen J. Wright +1
We study a class of generalized linear programs (GLP) in a large-scale setting, which includes simple, possibly nonsmooth convex regularizer and simple convex set constraints. By r…
Inexact Newton-CG Algorithms With Complexity Guarantees
Zhewei Yao, Peng Xu, Fred Roosta +2
We consider variants of a recently-developed Newton-CG algorithm for nonconvex problems \citep{royer2018newton} in which inexact estimates of the gradient and the Hessian informati…
Complexity of a Projected Newton-CG Method for Optimization with Bounds
Yue Xie, Stephen J. Wright
This paper describes a method for solving smooth nonconvex minimization problems subject to bound constraints with good worst-case complexity guarantees and practical performance.…
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
Chaobing Song, Stephen J. Wright, Jelena Diakonikolas
We study structured nonsmooth convex finite-sum optimization that appears widely in machine learning applications, including support vector machines and least absolute deviation. F…