Publications associées à Fast deterministic and randomized algorithms for low-rank approximation, matrix functions, and trace estimation

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Parallel algorithms for tensor completion in the CP format

Daniel Kressner, André Uschmajew

Low-rank tensor completion addresses the task of filling in missing entries in multidimensional data. It has proven its versatility in numerous applications, including context aware recommender systems and multivariate function learning. To handle large-sc ...

Elsevier Science Bv2016

Projection Methods For Large-Scale T-Sylvester Equations

Daniel Kressner

The matrix Sylvester equation for congruence, or T-Sylvester equation, has recently attracted considerable attention as a consequence of its close relation to palindromic eigenvalue problems. The theory concerning T-Sylvester equations is rather well under ...

Amer Mathematical Soc2016

A block algorithm for computing antitriangular factorizations of symmetric matrices

Daniel Kressner, Zvonimir Bujanovic

Any symmetric matrix can be reduced to antitriangular form in finitely many steps by orthogonal similarity transformations. This form reveals the inertia of the matrix and has found applications in, e.g., model predictive control and constraint preconditio ...

Springer2016

Optimal acoustic rake receiver

Martin Vetterli, Ivan Dokmanic, Robin Scheibler

An acoustic processing method for M acoustic receivers comprising the steps of: Determining a beamforming weight vector with M weights for the M acoustic receivers based on at least one the steering vector of at least one real acoustic source, on steering ...

2016

Compressive PCA for Low-Rank Matrices on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid

We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong to the span of the first few eigenvectors of the graphs constructed between ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Evaluating functions of positive-definite matrices using colored-noise thermostats

Michele Ceriotti, Michele Parrinello

Many applications in computational science require computing the elements of a function of a large matrix. A commonly used approach is based on the the evaluation of the eigenvalue decomposition, a task that, in general, involves a computing time that scal ...

Amer Physical Soc2014

Approximate Matrix Multiplication with Application to Linear Embeddings

Michail Vlachos, Anastasios Kyrillidis

In this paper, we study the problem of approximately computing the product of two real matrices. In particular, we analyze a dimensionality-reduction-based approximation algorithm due to Sarlos [1], introducing the notion of nuclear rank as the ratio of th ...

Ieee2014

Low-Rank Tensor Methods for Communicating Markov Processes

Daniel Kressner, Francisco Santos Paredes Quartin de Macedo

Stochastic models that describe interacting processes, such as stochastic automata networks, feature a dimensionality that grows exponentially with the number of processes. This state space explosion severely impairs the use of standard methods for the num ...

Springer-Verlag Berlin2014

Fast deterministic and randomized algorithms for low-rank approximation, matrix functions, and trace estimation

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Parallel algorithms for tensor completion in the CP format

Projection Methods For Large-Scale T-Sylvester Equations

A block algorithm for computing antitriangular factorizations of symmetric matrices

Optimal acoustic rake receiver

Compressive PCA for Low-Rank Matrices on Graphs

MATHICSE Technical Report: Fast recovery and approximation of hidden Cauchy structure

Evaluating functions of positive-definite matrices using colored-noise thermostats

Approximate Matrix Multiplication with Application to Linear Embeddings

Bivariate Matrix Functions

Low-Rank Tensor Methods for Communicating Markov Processes

MATHICSE Technical Report: Fast recovery and approximation of hidden Cauchy structure

Projection Methods For Large-Scale T-Sylvester Equations

Parallel algorithms for tensor completion in the CP format

Approximate Matrix Multiplication with Application to Linear Embeddings

A block algorithm for computing antitriangular factorizations of symmetric matrices

Compressive PCA for Low-Rank Matrices on Graphs

Evaluating functions of positive-definite matrices using colored-noise thermostats

Bivariate Matrix Functions

Low-Rank Tensor Methods for Communicating Markov Processes

Optimal acoustic rake receiver