Daniel Kressner
Professeur ordinaire
daniel.kressner@epfl.ch +41 21 693 25 46 http://anchp.epfl.ch
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See http://anchp.epfl.ch for more information.Publications
Publications Infoscience
Infoscience
Certified And Fast Computations With Shallow Covariance Kernels
Foundations Of Data Science. 2020-12-01. DOI : 10.3934/fods.2020022.Numerical Mathematics and Control Preface to a Special Issue Dedicated to Volker Mehrmann on the Occasion of his 65th Birthday
Vietnam Journal Of Mathematics. 2020-11-09. DOI : 10.1007/s10013-020-00451-x.Compress-and-restart block Krylov subspace methods for Sylvester matrix equations
Numerical Linear Algebra With Applications. 2020-10-13. DOI : 10.1002/nla.2339.Recursive blocked algorithms for linear systems with Kronecker product structure
Numerical Algorithms. 2020-07-01. DOI : 10.1007/s11075-019-00797-5.On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices
Linear Algebra And Its Applications. 2020-05-15. DOI : 10.1016/j.laa.2020.02.010.Low-Rank Approximation In The Frobenius Norm By Column And Row Subset Selection
Siam Journal On Matrix Analysis And Applications. 2020-01-01. DOI : 10.1137/19M1281848.Low-Rank Tensor Approximation for Chebyshev Interpolation in Parametric Option Pricing
Siam Journal On Financial Mathematics. 2020-01-01. DOI : 10.1137/19M1244172.hm-toolbox: MATLAB SOFTWARE FOR HODLR AND HSS MATRICES
Siam Journal On Scientific Computing. 2020-01-01. DOI : 10.1137/19M1288048.MATHICSE Technical Report : Low-rank approximation in the Frobenius norm by column and row subset selection
2019-08-18MATHICSE Technical Report : Low-rank updates and divide-andconquer methods for quadratic matrix equations
2019-03-12MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices
2019-02-12Numerical methods for option pricing: polynomial approximation and high dimensionality
Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-7386.hm-toolbox: Matlab software for HODLR and HSS matrices
2019Low-Rank Updates And A Divide-And-Conquer Method For Linear Matrix Equations
Siam Journal On Scientific Computing. 2019-01-01. DOI : 10.1137/17M1161038.7th Workshop on Matrix Equations and Tensor Techniques
Numerical Linear Algebra With Applications. 2018-12-01. DOI : 10.1002/nla.2223.MATHICSE Technical Report : Fast QR decomposition of HODLR matrices
2018-09-28MATHICSE Technical Report : A fast spectral divide-and-conquer method for banded matrices
2018-01-16Multigrid Methods Combined With Low-Rank Approximation For Tensor-Structured Markov Chains
Electronic Transactions On Numerical Analysis. 2018-01-01. DOI : 10.1553/etna_vol48s348.A Householder-Based Algorithm For Hessenberg-Triangular Reduction
Siam Journal On Matrix Analysis And Applications. 2018-01-01. DOI : 10.1137/17M1153637.SUBSPACE ACCELERATION FOR THE CRAWFORD NUMBER AND RELATED EIGENVALUE OPTIMIZATION PROBLEMS
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS. 2018. DOI : 10.1137/17M1127545.LOW-RANK UPDATES OF MATRIX FUNCTIONS
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS. 2018. DOI : 10.1137/17M1140108.FAST COMPUTATION OF THE MATRIX EXPONENTIAL FOR A TOEPLITZ MATRIX
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS. 2018. DOI : 10.1137/16M1083633.Fast hierarchical solvers for symmetric eigenvalue problems
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8808.Low-rank tensor methods for large Markov chains and forward feature selection methods
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-7718.Distributed Signal Processing via Chebyshev Polynomial Approximation
IEEE Transactions on Signal and Information Processing over Networks. 2018. DOI : 10.1109/TSIPN.2018.2824239.MATHICSE Technical Report : Low-rank updates and a divideand- conquer method for linear matrix equations
2017-12-13MATHICSE Technical Report : Incremental computation of block triangular matrix exponentials with application to option pricing
2017-02-28Recompression Of Hadamard Products Of Tensors In Tucker Format
Siam Journal On Scientific Computing. 2017. DOI : 10.1137/16M1093896.Fast Computation Of Spectral Projectors Of Banded Matrices
SIAM Journal On Matrix Analysis And Applications. 2017. DOI : 10.1137/16M1087278.Structure-Preserving Low Multilinear Rank Approximation Of Antisymmetric Tensors
SIAM Journal On Matrix Analysis And Applications. 2017. DOI : 10.1137/16M106618X.Multilevel tensor approximation of PDEs with random data
Stochastics And Partial Differential Equations-Analysis And Computations. 2017. DOI : 10.1007/s40072-017-0092-7.A Novel Iterative Method To Approximate Structured Singular Values
Siam Journal On Matrix Analysis And Applications. 2017. DOI : 10.1137/16M1074977.Learning heat diffusion graphs
IEEE Transactions on Signal and Information Processing over Networks. 2017. DOI : 10.1109/Tsipn.2017.2731164.MATHICSE Technical Report : Fast computation of spectral projectors of banded matrices
2016-08-01MATHICSE Technical Report : Perturbation of higher-order singular values
2016-07-01MATHICSE Technical Report : Fast computation of the matrix exponential for a Toeplitz matrix
2016-07-01MATHICSE Technical Report : Multilevel tensor approximation of PDEs with random data
2016-06-01MATHICSE Technical Report : A novel iterative method to approximate structured singular values
2016-05-17MATHICSE Technical Report : Multigrid methods combined with low-rank approximation for tensor structured Markov chains
2016-05-11MATHICSE Technical Report : Structure-preserving low multilinear rank approximation of antisymmetric tensors
2016-03-16Subspace Acceleration For Large-Scale Parameter-Dependent Hermitian Eigenproblems
Siam Journal On Matrix Analysis And Applications. 2016. DOI : 10.1137/15M1017181.Preconditioned Low-Rank Riemannian Optimization For Linear Systems With Tensor Product Structure
Siam Journal On Scientific Computing. 2016. DOI : 10.1137/15M1032909.Reduced Basis Methods: From Low-Rank Matrices To Low-Rank Tensors
Siam Journal On Scientific Computing. 2016. DOI : 10.1137/15M1042784.Projection Methods For Large-Scale T-Sylvester Equations
Mathematics Of Computation. 2016. DOI : 10.1090/mcom/3081.Parallel algorithms for tensor completion in the CP format
2016. 8th International Workshop on Parallel Matrix Algorithms and Applications (PMAA), Univ Svizzera Italiana, Lugano, SWITZERLAND, JUL 02-04, 2014. p. 222-234. DOI : 10.1016/j.parco.2015.10.002.Low-rank methods for parameter-dependent eigenvalue problems and matrix equations
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7137.Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6958.A block algorithm for computing antitriangular factorizations of symmetric matrices
Numerical Algorithms. 2016. DOI : 10.1007/s11075-015-9983-8.On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems
Linear Algebra And Its Applications. 2016. DOI : 10.1016/j.laa.2015.12.016.Tensor train approximation of moment equations for elliptic equations with lognormal coefficient
Computer Methods in Applied Mechanics and Engineering. 2016. DOI : 10.1016/j.cma.2016.05.026.MATHICSE Technical Report: Reduced basis methods: from low-rank matrices to low-rank tensor
2015-10-01MATHICSE Technical Report : Accelerated filtering on graphs using Lanczos method
2015-09-01MATHICSE Technical Report : Preconditioned low-rank Riemannian optimization for linear systems with tensor product structure
2015-07-01MATHICSE Technical Report : Subspace acceleration for large-scale parameter-dependent Hermitian eigenproblems
2015-04-01Algorithm 953: Parallel Library Software for the Multishift QR Algorithm with Aggressive Early Deflation
Acm Transactions On Mathematical Software. 2015. DOI : 10.1145/2699471.Numerical Mathematics and Advanced Applications - ENUMATH 2013
2015Accelerated filtering on graphs using Lanczos method
IEEE Signal Processing Letters. 2015.Truncated low-rank methods for solving general linear matrix equations
Numerical Linear Algebra With Applications. 2015. DOI : 10.1002/nla.1973.Low rank differential equations for Hamiltonian matrix nearness problems
Numerische Mathematik. 2015. DOI : 10.1007/s00211-014-0637-x.Adaptive polynomial approximation by means of random discrete least squares
2015. ENUMATH 2013, Lausanne, August 26-30, 2013. p. 547-554. DOI : 10.1007/978-3-319-10705-9_54.Low-rank tensor approximation for high-order correlation functions of Gaussian random fields
SIAM/ASA Journal of Uncertainty Quantification. 2015. DOI : 10.1137/140968938.MATHICSE Technical Report : Tensor train approximation of moment equations for the log-normal Darcy problem
2014-09-30MATHICSE Technical Report : Low-rank tensor approximation for high-order correlation functions of Gaussian random fields
2014-05-12A Parallel QZ Algorithm For Distributed Memory HPC Systems
SIAM Journal On Scientific Computing. 2014. DOI : 10.1137/140954817.Low-Rank Tensor Methods With Subspace Correction For Symmetric Eigenvalue Problems
SIAM Journal On Scientific Computing. 2014. DOI : 10.1137/130949919.Low-Rank Tensor Methods for Communicating Markov Processes
2014. 11th International Conference on Quantitative Evaluation of Systems (QEST), Florence, ITALY, SEP 08-10, 2014. p. 25-40. DOI : 10.1007/978-3-319-10696-0_4.Computing Extremal Points Of Symplectic Pseudospectra And Solving Symplectic Matrix Nearness Problems
SIAM Journal On Matrix Analysis And Applications. 2014. DOI : 10.1137/13094476X.On the eigenvalue decay of solutions to operator Lyapunov equations
Systems & Control Letters. 2014. DOI : 10.1016/j.sysconle.2014.09.006.Bivariate Matrix Functions
Operators And Matrices. 2014. DOI : 10.7153/oam-08-23.Nonlinear Eigenvalue Problems With Specified Eigenvalues
Siam Journal On Matrix Analysis And Applications. 2014. DOI : 10.1137/130927462.Low-rank tensor completion by Riemannian optimization
BIT Numerical Mathematics. 2014. DOI : 10.1007/s10543-013-0455-z.Memory-efficient Arnoldi algorithms for linearizations of matrix polynomials in Chebyshev basis
Numerical Linear Algebra With Applications. 2014. DOI : 10.1002/nla.1913.An indefinite variant of LOBPCG for definite matrix pencils
Numerical Algorithms. 2014. DOI : 10.1007/s11075-013-9754-3.On A Perturbation Bound For Invariant Subspaces Of Matrices
SIAM Journal On Matrix Analysis And Applications. 2014. DOI : 10.1137/130912372.Algorithm 941: htucker-A MATLAB Toolbox for Tensors in Hierarchical Tucker Format
ACM Transactions on Mathematical Software. 2014. DOI : 10.1145/2538688.Generalized eigenvalue problems with specified eigenvalues
IMA Journal Of Numerical Analysis. 2014. DOI : 10.1093/imanum/drt021.Optimally Packed Chains of Bulges in Multishift QR Algorithms
ACM Transactions on Mathematical Software. 2014. DOI : 10.1145/2559986.Subspace Methods For Computing The Pseudospectral Abscissa And The Stability Radius
SIAM Journal On Matrix Analysis And Applications. 2014. DOI : 10.1137/120869432.Dense and Structured Matrix Computations
Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6067.A preconditioned low-rank CG method for parameter-dependent Lyapunov matrix equations
Numerical Linear Algebra With Applications. 2013. DOI : 10.1002/nla.1919.An Error Analysis Of Galerkin Projection Methods For Linear Systems With Tensor Product Structure
SIAM Journal On Numerical Analysis. 2013. DOI : 10.1137/120900204.Structured Canonical Forms For Products Of (Skew-) Symmetric Matrices And The Matrix Equation XAX = B
Electronic Journal Of Linear Algebra. 2013. DOI : 10.13001/1081-3810.1651.Robust Solution Methods for Nonlinear Eigenvalue Problems
Lausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5920.Chebyshev interpolation for nonlinear eigenvalue problems
BIT Numerical Mathematics. 2012. DOI : 10.1007/s10543-012-0381-5.Accelerating Model Reduction of Large Linear Systems with Graphics Processors
2012. PARA, Reykjavik, Iceland, June 6-9, 2010. p. 88–97. DOI : 10.1007/978-3-642-28145-7_9.On aggressive early deflation in parallel variants of the QR algorithm
2012. PARA, Reykjavik, Iceland, June 6-9, 2010. p. 1–10. DOI : 10.1007/978-3-642-28151-8_1.Preconditioned Low-Rank Methods for High-Dimensional Elliptic PDE Eigenvalue Problems
Computational Methods in Applied Mathematics. 2011. DOI : 10.2478/cmam-2011-0020.Continuation of eigenvalues and invariant pairs for parameterized nonlinear eigenvalue problems
Numerische Mathematik. 2011. DOI : 10.1007/s00211-011-0392-1.Sparsity-seeking fusion of digital elevation models
2011Optimal similarity registration of volumentric images
2011Continuation of eigenvalues and invariant pairs for parameterized nonlinear eigenvalue problems
2011Linear dimension reduction for evolutionary data
2011Bivariate matrix functions
2011Low-rank tensor Krylov subspace methods for parametrized linear systems
SIAM Journal on Matrix Analysis and Applications. 2011. DOI : 10.1137/100799010.Linearization techniques for band structure calculations in absorbing photonic crystals
International Journal for Numerical Methods in Engineering. 2011. DOI : 10.1002/nme.3235.Structured eigenvalue condition numbers and linearizations for matrix polynomials
Linear Algebra and Its Applications. 2011. DOI : 10.1016/j.laa.2011.04.020.Computing Codimensions And Generic Canonical Forms For Generalized Matrix Products
Electronic Journal Of Linear Algebra. 2011. DOI : 10.13001/1081-3810.1440.A mixed-precision algorithm for the solution of Lyapunov equations on hybrid CPU-GPU platforms
Parallel Computing. 2011. DOI : 10.1016/j.parco.2010.12.002.Condensed forms for the symmetric eigenvalue problem on multi-threaded architectures
Concurrency Computation Practice and Experience. 2011. DOI : 10.1002/cpe.1680.Perturbation, extraction and refinement of invariant pairs for matrix polynomials
Linear Algebra and its Applications. 2011. DOI : 10.1016/j.laa.2010.06.029.Enseignement & Phd
Enseignement
Mathematics
Doctorants
Guido Margherita, He Haoze, Lam Hei Yin, Oehme Peter Bernd, Persson Ulf David, Shao Nian,A dirigé les thèses EPFL de
Cortinovis Alice , Effenberger Peter Cedric , Santos Paredes Quartin de Macedo Francisco , Shao Meiyue , Sirkovic Petar , Statti Francesco , Steinlechner Michael Maximilian , Strössner Christoph Max , Susnjara Ana , Séguin Axel Elie Joseph ,Cours
Algèbre linéaire avancée I
L'objectif du cours est d'introduire les notions de base de l'algèbre linéaire et de démontrer rigoureusement les résultats principaux de ce sujet.
Computational linear algebra
Ce cours donne un aperçu des techniques de pointe pour résoudre des problèmes d'algèbre linéaire de grande envergure qui se présentent généralement dans les applications. Un objectif central de ce cours est de donner la possibilité de choisir un algorithme approprié pour une application.