137 citations · 480 across the 19 of their papers we have counts for
8 papers · 1 filter
Multi-Level Composite Stochastic Optimization via Nested Variance Reduction
Junyu Zhang, Lin Xiao
We consider multi-level composite optimization problems where each mapping in the composition is the expectation over a family of random smooth mappings or the sum of some finite n…
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Lin Xiao, Adams Wei Yu, Qihang Lin +1
Machine learning with big data often involves large optimization models. For distributed optimization over a cluster of machines, frequent communication and synchronization of all…
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
Jialei Wang, Lin Xiao
We consider empirical risk minimization of linear predictors with convex loss functions. Such problems can be reformulated as convex-concave saddle point problems, and thus are wel…
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
Yuchen Zhang, Lin Xiao
We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We…
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
Lin Xiao, Tong Zhang
We consider the problem of minimizing the sum of two convex functions: one is the average of a large number of smooth component functions, and the other is a general convex functio…
A Randomized Nonmonotone Block Proximal Gradient Method for a Class of Structured Nonlinear Programming
Zhaosong Lu, Lin Xiao
We propose a randomized nonmonotone block proximal gradient (RNBPG) method for minimizing the sum of a smooth (possibly nonconvex) function and a block-separable (possibly nonconve…
On the Complexity Analysis of Randomized Block-Coordinate Descent Methods
Zhaosong Lu, Lin Xiao
In this paper we analyze the randomized block-coordinate descent (RBCD) methods proposed in [8,11] for minimizing the sum of a smooth convex function and a block-separable convex f…
A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem
Lin Xiao, Tong Zhang
We consider solving the -regularized least-squares (-LS) problem in the context of sparse recovery, for applications such as compressed sensing. The standard proxim…