2 citations · 4 across the 13 of their papers we have counts for
14 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…
An Inexact Proximal Framework for Nonsmooth Riemannian Difference-of-Convex Optimization
Bo Jiang, Meng Xu, Xingju Cai +1
Nonsmooth Riemannian optimization has attracted increasing attention, especially in problems with sparse structures. While existing formulations typically involve convex nonsmooth…
An Adaptive Proximal Inexact Gradient Framework and Its Application to Per-Antenna Constrained Joint Beamforming and Compression Design
Xilai Fan, Bo Jiang, Ya-Feng Liu
In this paper, we propose an adaptive proximal inexact gradient (APIG) framework for solving a class of nonsmooth composite optimization problems involving function and gradient er…
A New Adaptive Balanced Augmented Lagrangian Method with Application to ISAC Beamforming Design
Jiageng Wu, Bo Jiang, Xinxin Li +2
In this paper, we consider a class of convex programming problems with linear equality constraints, which finds broad applications in machine learning and signal processing. We pro…
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…
Joint Beamforming and Compression Design for Per-Antenna Power Constrained Cooperative Cellular Networks
Xilai Fan, Ya-Feng Liu, Bo Jiang
In the cooperative cellular network, relay-like base stations are connected to the central processor (CP) via rate-limited fronthaul links and the joint processing is performed at…
A Riemannian exponential augmented Lagrangian method for computing the projection robust Wasserstein distance
Bo Jiang, Ya-Feng Liu
Projecting the distance measures onto a low-dimensional space is an efficient way of mitigating the curse of dimensionality in the classical Wasserstein distance using optimal tran…
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…
Efficient Quantized Constant Envelope Precoding for Multiuser Downlink Massive MIMO Systems
Zheyu Wu, Ya-Feng Liu, Bo Jiang +1
Quantized constant envelope (QCE) precoding, a new transmission scheme that only discrete QCE transmit signals are allowed at each antenna, has gained growing research interests du…
Efficient CI-Based One-Bit Precoding for Multiuser Downlink Massive MIMO Systems with PSK Modulation
Zheyu Wu, Bo Jiang, Ya-Feng Liu +2
In this paper, we consider the one-bit precoding problem for the multiuser downlink massive multiple-input multiple-output (MIMO) system with phase shift keying (PSK) modulation. W…
A Novel Negative Penalty Approach for Multiuser One-Bit Massive MIMO Downlink with PSK Signaling
Zheyu Wu, Bo Jiang, Ya-Feng Liu +1
This paper considers the one-bit precoding problem for the multiuser downlink massive multiple-input multiple-output (MIMO) system with phase shift keying (PSK) modulation and focu…
Tightness and Equivalence of Semidefinite Relaxations for MIMO Detection
Ruichen Jiang, Ya-Feng Liu, Chenglong Bao +1
The multiple-input multiple-output (MIMO) detection problem, a fundamental problem in modern digital communications, is to detect a vector of transmitted symbols from the noisy out…