Publications related to Block algorithms for orthogonal symplectic factorizations

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Fast Computation Of Spectral Projectors Of Banded Matrices

Daniel Kressner, Ana Susnjara

We consider the approximate computation of spectral projectors for symmetric banded matrices. While this problem has received considerable attention, especially in the context of linear scaling electronic structure methods, the presence of small relative s ...

Siam Publications2017

Soft optical composites for tunable transmittance

Pedro Miguel Nunes Pereira de Almeida Reis, Sanjeev Kumar

We introduce a soft composite that is actuated mechanically to achieve switchable and tunable optical transmittance. Our design comprises a series of parallel opaque platelets embedded into an optically clear silicone-based elastomer matrix. Under an appli ...

2016

Fast recovery and approximation of hidden Cauchy structure

Robert Gerhard Jérôme Luce

We derive an algorithm of optimal complexity which determines whether a given matrix is a Cauchy matrix, and which exactly recovers the Cauchy points defining a Cauchy matrix from the matrix entries. Moreover, we study how to approximate a given matrix by ...

Elsevier Science Inc2016

Randomized Single-View Algorithms for Low-Rank Matrix Approximation

Volkan Cevher, Alp Yurtsever

This paper develops a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a sketch. These methods can preserve structural properties of the input matrix, such as positive-semideni ...

Under review2016

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

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

Low-rank Matrix Approximation Using Point-wise Operators

Arash Amini, Amin Karbasi

Extracting low dimensional structure from high dimensional data arises in many applications such as machine learning, statistical pattern recognition, wireless sensor networks, and data compression. If the data is restricted to a lower dimensional subspace ...

Institute of Electrical and Electronics Engineers2012

Optimizing over the Growing Spectrahedron

Martin Jaggi

We devise a framework for computing an approximate solution path for an important class of parameterized semidefinite problems that is guaranteed to be ε-close to the exact solution path. The problem of computing the entire regularization path for matrix f ...

Springer Berlin Heidelberg2012

Block algorithms for orthogonal symplectic factorizations

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Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data

Fast Computation Of Spectral Projectors Of Banded Matrices

Soft optical composites for tunable transmittance

Fast recovery and approximation of hidden Cauchy structure

Randomized Single-View Algorithms for Low-Rank Matrix Approximation

Parallel algorithms for tensor completion in the CP format

ALS Scheme using Extent-based Constraints for the Analysis of Chemical Reaction Systems

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

Low-rank Matrix Approximation Using Point-wise Operators

Optimizing over the Growing Spectrahedron

Soft optical composites for tunable transmittance

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data

Fast Computation Of Spectral Projectors Of Banded Matrices

Optimizing over the Growing Spectrahedron

Fast recovery and approximation of hidden Cauchy structure

Randomized Single-View Algorithms for Low-Rank Matrix Approximation

Parallel algorithms for tensor completion in the CP format

ALS Scheme using Extent-based Constraints for the Analysis of Chemical Reaction Systems

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

Low-rank Matrix Approximation Using Point-wise Operators