3 citations · 3 across the 8 of their papers we have counts for
9 papers
Riemannian Difference-of-Convex Optimization for K-Means Clustering
Meng Xu, Bo Jiang, Hanfu Zhang +2
K-means is a widely adopted clustering approach in signal processing and machine learning. In this paper, we study K-means clustering through a cardinality-constrained formulation…
A Stochastic Riemannian Alternating Descent Ascent Method for Nonsmooth Composite Expectation Optimization on Riemannian Manifolds
Meng Xu, Bo Jiang, Ya-Feng Liu +1
In this paper, we consider a class of Riemannian nonsmooth composite expectation optimization problems, which arises in various machine learning, signal processing, and statistics…
Optimal Low-Dimensional Structures of ISAC Beamforming: Theory and Efficient Algorithms
Xiaotong Zhao, Mian Li, Ya-Feng Liu +2
Transmit beamforming design is a fundamental problem in integrated sensing and communication (ISAC) systems. Numerous methods have been proposed to jointly optimize key performance…
On the Oracle Complexity of a Riemannian Inexact Augmented Lagrangian Method for Riemannian Nonsmooth Composite Problems
Meng Xu, Bo Jiang, Ya-Feng Liu +1
In this paper, we establish for the first time the oracle complexity of a Riemannian inexact augmented Lagrangian (RiAL) method with the classical dual update for solving a class o…
A Riemannian Alternating Descent Ascent Algorithmic Framework for Nonconvex-Linear Minimax Problems on Riemannian Manifolds
Meng Xu, Bo Jiang, Ya-Feng Liu +1
In this paper, we consider a class of nonconvex-linear minimax problems on Riemannian manifolds, which find wide applications in machine learning and signal processing. For solving…
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
Jiaojiao Zhang, Jiang Hu, Anthony Man-Cho So +1
Many machine learning tasks, such as principal component analysis and low-rank matrix completion, give rise to manifold optimization problems. Although there is a large body of wor…
Extreme Point Pursuit -- Part II: Further Error Bound Analysis and Applications
Junbin Liu, Ya Liu, Wing-Kin Ma +2
In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems. In particular, the error bound techniques wer…
A Survey of Recent Advances in Optimization Methods for Wireless Communications
Ya-Feng Liu, Tsung-Hui Chang, Mingyi Hong +4
Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a s…
An Efficient Alternating Riemannian/Projected Gradient Descent Ascent Algorithm for Fair Principal Component Analysis
Meng Xu, Bo Jiang, Wenqiang Pu +2
Fair principal component analysis (FPCA), a ubiquitous dimensionality reduction technique in signal processing and machine learning, aims to find a low-dimensional representation f…