9 citations · 22 across the 10 of their papers we have counts for
6 papers · 1 filter
Augmented Lagrangian Methods for Time-varying Constrained Online Convex Optimization
Haoyang Liu, Xiantao Xiao, Liwei Zhang
In this paper, we consider online convex optimization (OCO) with time-varying loss and constraint functions. Specifically, the decision maker chooses sequential decisions based onl…
Regrets of Proximal Method of Multipliers for Online Non-convex Optimization with Long Term Constraints
Liwei Zhang, Haoyang Liu, Xiantao Xiao
The online optimization problem with non-convex loss functions over a closed convex set, coupled with a set of inequality (possibly non-convex) constraints is a challenging online…
A Decomposition Augmented Lagrangian Method for Low-rank Semidefinite Programming
Yifei Wang, Kangkang Deng, Haoyang Liu +1
We develop a decomposition method based on the augmented Lagrangian framework to solve a broad family of semidefinite programming problems, possibly with nonlinear objective functi…
Low-rank Matrix Optimization Using Polynomial-filtered Subspace Extraction
Yongfeng Li, Haoyang Liu, Zaiwen Wen +1
In this paper, we study first-order methods on a large variety of low-rank matrix optimization problems, whose solutions only live in a low dimensional eigenspace. Traditional firs…
An equivalence between critical points for rank constraints versus low-rank factorizations
Wooseok Ha, Haoyang Liu, Rina Foygel Barber
Two common approaches in low-rank optimization problems are either working directly with a rank constraint on the matrix variable, or optimizing over a low-rank factorization so th…
A Sparse Completely Positive Relaxation of the Modularity Maximization for Community Detection
Junyu Zhang, Haoyang Liu, Zaiwen Wen +1
In this paper, we consider the community detection problem under either the stochastic block model (SBM) assumption or the degree-correlated stochastic block model (DCSBM) assumpti…