Publications de Daniel Kressner

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MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

Daniel Kressner, Stefano Massei, Alice Cortinovis

The problem of finding a

k\times k

submatrix of maximum volume of a matrix A is of interest in a variety of applications. For example, it yields a quasi-best low-rank approximation constructed from the rows and columns of A. We show that such a submatrix ...

MATHICSE2019

Daniel Kressner

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Recursive blocked algorithms for linear systems with Kronecker product structure

Certified And Fast Computations With Shallow Covariance Kernels

Low-Rank Updates And A Divide-And-Conquer Method For Linear Matrix Equations

MATHICSE Technical Report : Low-rank approximation in the Frobenius norm by column and row subset selection

MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

hm-toolbox: Matlab software for HODLR and HSS matrices

MATHICSE Technical Report : Low-rank updates and divide-andconquer methods for quadratic matrix equations

Multigrid Methods Combined With Low-Rank Approximation For Tensor-Structured Markov Chains

A Householder-Based Algorithm For Hessenberg-Triangular Reduction

hm-toolbox: Matlab software for HODLR and HSS matrices

MATHICSE Technical Report : Low-rank updates and divide-andconquer methods for quadratic matrix equations

MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

MATHICSE Technical Report : Low-rank approximation in the Frobenius norm by column and row subset selection

A Householder-Based Algorithm For Hessenberg-Triangular Reduction

Low-Rank Updates And A Divide-And-Conquer Method For Linear Matrix Equations

Certified And Fast Computations With Shallow Covariance Kernels

Multigrid Methods Combined With Low-Rank Approximation For Tensor-Structured Markov Chains

Recursive blocked algorithms for linear systems with Kronecker product structure