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Transmit beamforming for physical-layer multicasting

This paper considers the problem of downlink transmit beamforming for wireless transmission and downstream precoding for digital subscriber wireline transmission, in the context of common information…
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Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

A joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN) while exploiting theDeep neural network's ability to approximate any nonlinear function is proposed.
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Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups

It is shown that Lagrangian relaxation coupled with suitable randomization/cochannel multicast power control yield computationally efficient high-quality approximate solutions.
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Blind PARAFAC receivers for DS-CDMA systems

This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool…
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Tensor Decomposition for Signal Processing and Machine Learning

The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties; broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from source separation to collaborative filtering, mixture and topic modeling, classification, and multilinear subspace learning.
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Parallel factor analysis in sensor array processing

This link facilitates the derivation of powerful identifiability results for MI-SAP, shows that the uniqueness of single- and multiple-invariance ESPRIT stems from uniqueness of low-rank decomposition of three-way arrays, and allows tapping on the available expertise for fitting the PARAFAC model.
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SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication

Multi-dimensional arrays, or tensors, are increasingly found in fields such as signal processing and recommender systems. Real-world tensors can be enormous in size and often very sparse. There is a…
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Convex approximation techniques for joint multiuser downlink beamforming and admission control

This work advocates a cross-layer approach to joint multiuser transmit beamforming and admission control, aiming to maximize the number of users that can be served at their desired QoS.
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Consensus-ADMM for General Quadratically Constrained Quadratic Programming

The core components are carefully designed to make the overall algorithm more scalable, including efficient methods for solving QCQP-1, memory efficient implementation, parallel/distributed implementation, and smart initialization.
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A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization

A hybrid between alternating optimization and the alternating direction method of multipliers, each matrix factor is updated in turn, using ADMM, hence the name AO-ADMM, which can naturally accommodate a great variety of constraints on the factor matrices, and almost all possible loss measures for the fitting.
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