26 citations · 55 across the 15 of their papers we have counts for
8 papers · 1 filter
First-order algorithms converge faster than on convex problems
Ching-pei Lee, Stephen J. Wright
It is well known that both gradient descent and stochastic coordinate descent achieve a global convergence rate of in the objective value, when applied to a scheme for min…
Inexact Variable Metric Stochastic Block-Coordinate Descent for Regularized Optimization
Ching-pei Lee, Stephen J. Wright
Block-coordinate descent (BCD) is a popular framework for large-scale regularized optimization problems with block-separable structure. Existing methods have several limitations. T…
Random Sampling and Efficient Algorithms for Multiscale PDEs
Ke Chen, Qin Li, Jianfeng Lu +1
We describe a numerical framework that uses random sampling to efficiently capture low-rank local solution spaces of multiscale PDE problems arising in domain decomposition. In con…
Randomness and Permutations in Coordinate Descent Methods
Mert Gurbuzbalaban, Asuman Ozdaglar, Nuri Denizcan Vanli +1
We consider coordinate descent (CD) methods with exact line search on convex quadratic problems. Our main focus is to study the performance of the CD method that use random permuta…
A Newton-CG Algorithm with Complexity Guarantees for Smooth Unconstrained Optimization
Clément W. Royer, Michael O'Neill, Stephen J. Wright
We consider minimization of a smooth nonconvex objective function using an iterative algorithm based on Newton's method and the linear conjugate gradient algorithm, with explicit d…
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
Ching-pei Lee, Cong Han Lim, Stephen J. Wright
We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving ERM problems with a nonsmooth regularization ter…