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Matrix-Free Inexact Preconditioning Techniques for Isogeometric Tensor-Product Discretizations. [PDF]

J Sci Comput
Mika MŁ, Hiemstra RR, Schillinger D.
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A converse to low-rank matrix completion

2016 IEEE International Symposium on Information Theory (ISIT), 2016
In many practical applications, one is given a subset Ω of the entries in a d × N data matrix X, and aims to infer all the missing entries. Existing theory in low-rank matrix completion (LRMC) provides conditions on X (e.g., bounded coherence or genericity) and Ω (e.g., uniform random sampling or deterministic combinatorial conditions) to guarantee ...
Daniel L. Pimentel-Alarcón, Robert D. Nowak   +1 more
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Decentralized low-rank matrix completion

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
This paper introduces algorithms for the decentralized low-rank matrix completion problem. Assume a low-rank matrix W = [W 1 ,W 2 , …,W L ]. In a network, each agent l observes some entries of W l . In order to recover the unobserved entries of W via decentralized computation, we factorize the unknown matrix W as the product of a public matrix X ...
Qing Ling 0001, Yangyang Xu, Wotao Yin, Zaiwen Wen   +3 more
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Low-Rank Matrix Completion by Riemannian Optimization

SIAM Journal on Optimization, 2013
The matrix completion problem consists of finding or approximating a low-rank matrix based on a few samples of this matrix. We propose a new algorithm for matrix completion that minimizes the least-square distance on the sampling set over the Riemannian manifold of fixed-rank matrices.
Bart Vandereycken
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Adaptive Low Rank Matrix Completion

IEEE Transactions on Signal Processing, 2017
The low-rank matrix completion problem is fundamental to a number of tasks in data mining, machine learning, and signal processing. This paper considers the problem of adaptive matrix completion in time-varying scenarios. Given a sequence of incomplete and noise-corrupted matrices, the goal is to recover and track the underlying low rank matrices ...
Ruchi Tripathi, Boda Mohan, Ketan Rajawat   +2 more
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Structural Identifiability in Low-Rank Matrix Factorization

Algorithmica, 2009
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Epameinondas Fritzilas, Martin Milanic, Sven Rahmann, Yasmín Á. Ríos-Solís   +3 more
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Low-Rank Matrix Approximation with Manifold Regularization

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
This paper proposes a new model of low-rank matrix factorization that incorporates manifold regularization to the matrix factorization. Superior to the graph-regularized nonnegative matrix factorization, this new regularization model has globally optimal and closed-form solutions.
Zhenyue Zhang, Keke Zhao
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Fast computation of low rank matrix approximations

Proceedings of the thirty-third annual ACM symposium on Theory of computing, 2001
Given a matrix A , it is often desirable to find a good approximation to A that has low rank. We introduce a simple technique for accelerating the computation of such approximations when A has strong spectral features, that is, when the singular values of interest are ...
Dimitris Achlioptas, Frank McSherry
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A Schur Method for Low-Rank Matrix Approximation

SIAM Journal on Matrix Analysis and Applications, 1996
Summary: The usual way to compute a low-rank approximant of a matrix \(H\) is to take its singular value decomposition (SVD) and truncate it by setting the small singular values equal to 0. However, the SVD is computationally expensive. This paper describes a much simpler generalized Schur-type algorithm to compute similar low-rank approximants.
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Low-Rank Structured Covariance Matrix Estimation

IEEE Signal Processing Letters, 2019
The covariance matrix estimation problem is posed in both the Bayesian and frequentist settings as the solution of a maximum a posteriori (MAP) or maximum likelihood (ML) optimization, respectively, when the true covariance consists of a known (or bounded) noise floor and a low-rank component. Persymmetric structure may also be assumed.
Azer P. Shikhaliev, Lee C. Potter, Yuejie Chi   +2 more
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