6 papers
Accelerated primal dual fixed point algorithm
Ya-Nan Zhu
This work proposes an Accelerated Primal-Dual Fixed-Point (APDFP) method that employs Nesterov type acceleration to solve composite problems of the form min f(x) + g(Bx), where g i…
A Single-Mode Quasi Riemannian Gradient Descent Algorithm for Low-Rank Tensor Recovery
Yuanwei Zhang, Ya-Nan Zhu, Xiaoqun Zhang
This paper focuses on recovering a low-rank tensor from its incomplete measurements. We propose a novel algorithm termed the Single Mode Quasi Riemannian Gradient Descent (SM-QRGD)…
Compressing MIMO Channel Submatrices with Tucker Decomposition: Enabling Efficient Storage and Reducing SINR Computation Overhead
Yuanwei Zhang, Ya-Nan Zhu, Xiaoqun Zhang
Massive multiple-input multiple-output (MIMO) systems employ a large number of antennas to achieve gains in capacity, spectral efficiency, and energy efficiency. However, the large…
Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstruction
Zhao He, Ya-Nan Zhu, Suhao Qiu +2
Objective: Interventional MRI (i-MRI) is crucial for MR image-guided therapy. Current image reconstruction methods for dynamic MR imaging are mostly retrospective that may not be s…
A Stochastic Variance Reduced Primal Dual Fixed Point Method For Linearly Constrained Separable Optimization
Ya-Nan Zhu, Xiaoqun Zhang
In this paper we combine the stochastic variance reduced gradient (SVRG) method [17] with the primal dual fixed point method (PDFP) proposed in [7] to solve a sum of two convex fun…
Stochastic primal dual fixed point method for composite optimization
YaNanZhu, XiaoqunZhang
In this paper we propose a stochastic primal dual fixed point method (SPDFP) for solving the sum of two proper lower semi-continuous convex function and one of which is composite.…