45 citations · 103 across the 15 of their papers we have counts for
15 papers
On the Convergence Rate of the Duality Gap in the Generalized Frank-Wolfe Method Under the Weak Growth Condition
Renbo Zhao
We analyze the convergence rate of certain sequence of the duality gaps in the generalized Frank-Wolfe method, under certain regime of the weak growth condition proposed in Peña (2…
An Away-Step Frank-Wolfe Method for Minimizing Logarithmically-Homogeneous Barriers
Renbo Zhao
We present and analyze an away-step Frank-Wolfe method for the convex optimization problem , where is a …
The Generalized Multiplicative Gradient Method for A Class of Convex Optimization Problems Over Symmetric Cones
Renbo Zhao
We develop and analyze the Generalized Multiplicative Gradient (GMG) method for solving a class of convex optimization problems over symmetric cones, where the objective function d…
Convergence Rate Analysis of the Multiplicative Gradient Method for PET-Type Problems
Renbo Zhao
We analyze the convergence rate of the multiplicative gradient (MG) method for PET-type problems with component functions and an -dimensional optimization variable. We show…
A Generalized Frank-Wolfe Method With "Dual Averaging" for Strongly Convex Composite Optimization
Renbo Zhao, Qiuyun Zhu
We propose a simple variant of the generalized Frank-Wolfe method for solving strongly convex composite optimization problems, by introducing an additional averaging step on the du…
Analysis of the Frank-Wolfe Method for Convex Composite Optimization involving a Logarithmically-Homogeneous Barrier
Renbo Zhao, Robert M. Freund
We present and analyze a new generalized Frank-Wolfe method for the composite optimization problem , where is a -loga…
A Primal-Dual Smoothing Framework for Max-Structured Non-Convex Optimization
Renbo Zhao
We propose a primal-dual smoothing framework for finding a near-stationary point of a class of non-smooth non-convex optimization problems with max-structure. We analyze the primal…
Accelerated Stochastic Algorithms for Convex-Concave Saddle-Point Problems
Renbo Zhao
We develop stochastic first-order primal-dual algorithms to solve a class of convex-concave saddle-point problems. When the saddle function is strongly convex in the primal variabl…
An Inexact Primal-Dual Smoothing Framework for Large-Scale Non-Bilinear Saddle Point Problems
Le Thi Khanh Hien, Renbo Zhao, William B. Haskell
We develop an inexact primal-dual first-order smoothing framework to solve a class of non-bilinear saddle point problems with primal strong convexity. Compared with existing method…
A Unified Framework for Stochastic Matrix Factorization via Variance Reduction
Renbo Zhao, William B. Haskell, Jiashi Feng
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction. Our framework is general and it subsumes several we…
Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies
Renbo Zhao, William B. Haskell, Vincent Y. F. Tan
We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By proposing a new framework for the convergence analysis, we prove improved convergence rates and computational c…
A Unified Convergence Analysis of the Multiplicative Update Algorithm for Regularized Nonnegative Matrix Factorization
Renbo Zhao, Vincent Y. F. Tan
The multiplicative update (MU) algorithm has been extensively used to estimate the basis and coefficient matrices in nonnegative matrix factorization (NMF) problems under a wide ra…
Online Nonnegative Matrix Factorization with General Divergences
Renbo Zhao, Vincent Y. F. Tan, Huan Xu
We develop a unified and systematic framework for performing online nonnegative matrix factorization under a wide variety of important divergences. The online nature of our algorit…
Online Nonnegative Matrix Factorization with Outliers
Renbo Zhao, Vincent Y. F. Tan
We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scal…
Adversarial Top- Ranking
Changho Suh, Vincent Y. F. Tan, Renbo Zhao
We study the top- ranking problem where the goal is to recover the set of top- ranked items out of a large collection of items based on partially revealed preferences. We con…