Publications related to MATHICSE Technical Report : A fast spectral divide-and-conquer method for banded matrices

Fast Computation Of Spectral Projectors Of Banded Matrices

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

MATHICSE Technical Report : Low-rank updates and a divideand- conquer method for linear matrix equations

Daniel Kressner, Stefano Massei

Linear matrix equations, such as the Sylvester and Lyapunov equations, play an important role in various applications, including the stability analysis and dimensionality reduction of linear dynamical control systems and the solution of partial differentia ...

MATHICSE2017

Recompression Of Hadamard Products Of Tensors In Tucker Format

Daniel Kressner, Lana Perisa

The Hadamard product features prominently in tensor-based algorithms in scientific computing and data analysis. Due to its tendency to significantly increase ranks, the Hadamard product can represent a major computational obstacle in algorithms based on lo ...

Siam Publications2017

MATHICSE Technical Report : 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 ...

MATHICSE2016

Convex block-sparse linear regression with expanders - provably

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis, Luca Baldassarre, Bubacarr Bah

Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-ti ...

2016

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

Dominique Bonvin, Julien Léo Billeter, Michael Amrhein

Multivariate curve resolution via alternating least squares (ALS) is used to resolve the concentration profiles C and the pure component spectra E of S species from the multivariate absorbance data A, assuming the bilinear model ...

2016

MATHICSE Technical Report : Fast computation of the matrix exponential for a Toeplitz matrix

Daniel Kressner, Robert Gerhard Jérôme Luce

The computation of the matrix exponential is a ubiquitous operation in numerical mathematics, and for a general, unstructured n×n matrix it can be computed in O(n3) operations. An interesting problem arises if the input matrix is a Toeplitz matrix, for exa ...

MATHICSE2016

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

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first

k

eigenvectors of the similarity matrix' Laplacian, whose computati ...

Ieee2016

A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain

Martin Vetterli, Robin Scheibler, Saeid Haghighatshoar

In this paper, we design a new iterative low-complexity algorithm for computing the Walsh-Hadamard transform (WHT) of an N dimensional signal with a K-sparse WHT. We suppose that N is a power of two and K = O(N^α), scales sub-linearly in N for some α ∈ (0, ...

Institute of Electrical and Electronics Engineers2015

MATHICSE Technical Report : A fast spectral divide-and-conquer method for banded matrices

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Fast Computation Of Spectral Projectors Of Banded Matrices

MATHICSE Technical Report : Low-rank updates and a divideand- conquer method for linear matrix equations

Recompression Of Hadamard Products Of Tensors In Tucker Format

MATHICSE Technical Report : Fast computation of spectral projectors of banded matrices

Convex block-sparse linear regression with expanders - provably

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

MATHICSE Technical Report : Fast computation of the matrix exponential for a Toeplitz matrix

Fast recovery and approximation of hidden Cauchy structure

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain

Convex block-sparse linear regression with expanders - provably

A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain

Fast Computation Of Spectral Projectors Of Banded Matrices

Recompression Of Hadamard Products Of Tensors In Tucker Format

MATHICSE Technical Report : Fast computation of spectral projectors of banded matrices

Fast recovery and approximation of hidden Cauchy structure

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

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

MATHICSE Technical Report : Low-rank updates and a divideand- conquer method for linear matrix equations

MATHICSE Technical Report : Fast computation of the matrix exponential for a Toeplitz matrix