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Adaptive Alternating Minimization Algorithms [PDF]
2007 IEEE International Symposium on Information Theory, 2007 The classical alternating minimization (or projection) algorithm has been successful in the context of solving optimization problems over two variables. The iterative nature and simplicity of the algorithm has led to its application to many areas such as signal processing, information theory, control, and finance. A general set of sufficient conditions
Urs Niesen +2 more
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Low-Complexity Self-Interference Cancellation for Multiple Access Full Duplex Systems
Sensors, 2022 Self-interference occurs when there is electromagnetic coupling between the transmission and reception of the same node; thus, degrading the RX sensitivity to incoming signals.
Shachar Shayovitz +2 more
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Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction. [PDF]
PLoS ONE, 2016 Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of ...
Hanming Zhang +5 more
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Phase Retrieval Using Alternating Minimization [PDF]
IEEE Transactions on Signal Processing, 2015 Accepted for publication in IEEE Transactions on Signal ...
Praneeth Netrapalli +2 more
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An alternating minimization algorithm for Factor Analysis [PDF]
Kybernetika, 2019 The problem of decomposing a given covariance matrix as the sum of a positive semi-definite matrix of given rank and a positive semi-definite diagonal matrix, is considered. We present a projection-type algorithm to address this problem. This algorithm appears to perform extremely well and is extremely fast even when the given covariance matrix has a ...
Valentina Ciccone +2 more
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Axioms, 2023 This paper considers a 1D time-domain inverse scattering problem for the Helmholtz equation in which penetrable scatterers are to be determined from boundary measurements of the scattering data.
Nguyen Trung Thành
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Multi-Channel Blind Restoration of Mixed Noise Images under Atmospheric Turbulence
Atmosphere, 2022 The imaging quality of astronomical or space objects is significantly degraded by atmospheric turbulence, photon noise, image sensor noise, and other factors.
Huizhen Yang +4 more
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Sparse Non-negative Matrix Factorization Algorithm Based on Proximal Alternating Linearized Minimization [PDF]
Jisuanji gongcheng, 2019 This paper combinessparsity constraint and Proximal Alternating Linearized Minimization(PALM),proposes a Sparse Non-negative Matrix Factorization(SNMF) algorithm,called SNMF_PALM.The non-convex Smoothly Clipped Absolute Deviation(SCAD) function is used ...
WANG Jing,YANG Dan
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Fronthaul Compression for Uplink Massive MIMO Using Matrix Decomposition
IEEE Open Journal of the Communications Society, 2023 Massive multiple-input-multiple-output (MIMO) is a key enabler for obtaining higher data rates in the next generation wireless technology. While it has the power to transform cellular communication, with potential for spatial diversity and multiplexing ...
P. Aswathylakshmi, Radha Krishna Ganti
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Truncated Fractional-Order Total Variation for Image Denoising under Cauchy Noise
Axioms, 2022 In recent years, the fractional-order derivative has achieved great success in removing Gaussian noise, impulsive noise, multiplicative noise and so on, but few works have been conducted to remove Cauchy noise. In this paper, we propose a novel nonconvex
Jianguang Zhu +3 more
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