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A Single-Mode Quasi Riemannian Gradient Descent Algorithm for Low-Rank Tensor Recovery

Autor: Zhang, Yuanwei, Zhu, Ya-Nan, Zhang, Xiaoqun

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). By exploiting the benefits of both fixed-rank matrix tangent space p

Externí odkaz: http://arxiv.org/abs/2401.15925

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Compressing MIMO Channel Submatrices with Tucker Decomposition: Enabling Efficient Storage and Reducing SINR Computation Overhead

Autor: Zhang, Yuanwei, Zhu, Ya-Nan, Zhang, Xiaoqun

Publikováno v: CSIAM Transaction on Applied Mathematics, 5 (2024), pp. 615-635

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 antenna array also incurs substantial storage and computational costs

Externí odkaz: http://arxiv.org/abs/2401.09792

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Federated Primal Dual Fixed Point Algorithm

Autor: Zhu, Ya-Nan, Liang, Jingwei, Zhang, Xiaoqun

Federated learning (FL) is a distributed learning paradigm that allows several clients to learn a global model without sharing their private data. In this paper, we generalize a primal dual fixed point (PDFP) \cite{PDFP} method to federated learning

Externí odkaz: http://arxiv.org/abs/2305.13604

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On the convergence of continuous and discrete unbalanced optimal transport models

Autor: Xiong, Zhe, Li, Lei, Zhu, Ya-Nan, Zhang, Xiaoqun

We consider a Beckmann formulation of an unbalanced optimal transport (UOT) problem. The $\Gamma$-convergence of this formulation of UOT to the corresponding optimal transport (OT) problem is established as the balancing parameter $\alpha$ goes to in

Externí odkaz: http://arxiv.org/abs/2303.17267

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