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. 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. DOI : 10.1007/s10013-020-00451-x.Compress-and-restart block Krylov subspace methods for Sylvester matrix equations
Numerical Linear Algebra With Applications. 2020. DOI : 10.1002/nla.2339.Recursive blocked algorithms for linear systems with Kronecker product structure
Numerical Algorithms. 2020. 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. DOI : 10.1016/j.laa.2020.02.010.Low-Rank Tensor Approximation for Chebyshev Interpolation in Parametric Option Pricing
Siam Journal On Financial Mathematics. 2020. DOI : 10.1137/19M1244172.Low-Rank Approximation In The Frobenius Norm By Column And Row Subset Selection
Siam Journal On Matrix Analysis And Applications. 2020. DOI : 10.1137/19M1281848.hm-toolbox: MATLAB SOFTWARE FOR HODLR AND HSS MATRICES
Siam Journal On Scientific Computing. 2020. DOI : 10.1137/19M1288048.MATHICSE Technical Report : Low-rank approximation in the Frobenius norm by column and row subset selection
2019MATHICSE Technical Report : Low-rank updates and divide-andconquer methods for quadratic matrix equations
2019MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices
2019Low-Rank Updates And A Divide-And-Conquer Method For Linear Matrix Equations
Siam Journal On Scientific Computing. 2019. DOI : 10.1137/17M1161038.hm-toolbox: Matlab software for HODLR and HSS matrices
2019Numerical methods for option pricing: polynomial approximation and high dimensionality
Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-7386.7th Workshop on Matrix Equations and Tensor Techniques
Numerical Linear Algebra With Applications. 2018. DOI : 10.1002/nla.2223.MATHICSE Technical Report : Fast QR decomposition of HODLR matrices
2018MATHICSE Technical Report : A fast spectral divide-and-conquer method for banded matrices
2018Multigrid Methods Combined With Low-Rank Approximation For Tensor-Structured Markov Chains
Electronic Transactions On Numerical Analysis. 2018. DOI : 10.1553/etna_vol48s348.A Householder-Based Algorithm For Hessenberg-Triangular Reduction
Siam Journal On Matrix Analysis And Applications. 2018. DOI : 10.1137/17M1153637.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
2017MATHICSE Technical Report : Incremental computation of block triangular matrix exponentials with application to option pricing
2017Recompression Of Hadamard Products Of Tensors In Tucker Format
Siam Journal On Scientific Computing. 2017. DOI : 10.1137/16M1093896.A Novel Iterative Method To Approximate Structured Singular Values
Siam Journal On Matrix Analysis And Applications. 2017. DOI : 10.1137/16M1074977.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.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
2016MATHICSE Technical Report : Perturbation of higher-order singular values
2016MATHICSE Technical Report : Fast computation of the matrix exponential for a Toeplitz matrix
2016MATHICSE Technical Report : Multilevel tensor approximation of PDEs with random data
2016MATHICSE Technical Report : A novel iterative method to approximate structured singular values
2016MATHICSE Technical Report : Multigrid methods combined with low-rank approximation for tensor structured Markov chains
2016MATHICSE Technical Report : Structure-preserving low multilinear rank approximation of antisymmetric tensors
2016Parallel 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.A block algorithm for computing antitriangular factorizations of symmetric matrices
Numerical Algorithms. 2016. DOI : 10.1007/s11075-015-9983-8.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.Subspace Acceleration For Large-Scale Parameter-Dependent Hermitian Eigenproblems
Siam Journal On Matrix Analysis And Applications. 2016. DOI : 10.1137/15M1017181.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.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.Preconditioned Low-Rank Riemannian Optimization For Linear Systems With Tensor Product Structure
Siam Journal On Scientific Computing. 2016. DOI : 10.1137/15M1032909.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
2015MATHICSE Technical Report : Accelerated filtering on graphs using Lanczos method
2015MATHICSE Technical Report : Preconditioned low-rank Riemannian optimization for linear systems with tensor product structure
2015MATHICSE Technical Report : Subspace acceleration for large-scale parameter-dependent Hermitian eigenproblems
2015Accelerated filtering on graphs using Lanczos method
2015Truncated low-rank methods for solving general linear matrix equations
Numerical Linear Algebra With Applications. 2015. DOI : 10.1002/nla.1973.Algorithm 953: Parallel Library Software for the Multishift QR Algorithm with Aggressive Early Deflation
Acm Transactions On Mathematical Software. 2015. DOI : 10.1145/2699471.Low-rank tensor approximation for high-order correlation functions of Gaussian random fields
SIAM/ASA Journal of Uncertainty Quantification. 2015. DOI : 10.1137/140968938.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.Numerical Mathematics and Advanced Applications - ENUMATH 2013
2015MATHICSE Technical Report : Tensor train approximation of moment equations for the log-normal Darcy problem
2014MATHICSE Technical Report : Low-rank tensor approximation for high-order correlation functions of Gaussian random fields
2014Memory-efficient Arnoldi algorithms for linearizations of matrix polynomials in Chebyshev basis
Numerical Linear Algebra With Applications. 2014. DOI : 10.1002/nla.1913.Nonlinear Eigenvalue Problems With Specified Eigenvalues
Siam Journal On Matrix Analysis And Applications. 2014. DOI : 10.1137/130927462.On A Perturbation Bound For Invariant Subspaces Of Matrices
SIAM Journal On Matrix Analysis And Applications. 2014. DOI : 10.1137/130912372.Dense and Structured Matrix Computations : the Parallel QR Algorithm and Matrix Exponentials
Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6067.An indefinite variant of LOBPCG for definite matrix pencils
Numerical Algorithms. 2014. DOI : 10.1007/s11075-013-9754-3.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.A Parallel QZ Algorithm For Distributed Memory HPC Systems
SIAM Journal On Scientific Computing. 2014. DOI : 10.1137/140954817.On the eigenvalue decay of solutions to operator Lyapunov equations
Systems & Control Letters. 2014. DOI : 10.1016/j.sysconle.2014.09.006.Generalized eigenvalue problems with specified eigenvalues
IMA Journal Of Numerical Analysis. 2014. DOI : 10.1093/imanum/drt021.Low-rank tensor completion by Riemannian optimization
BIT Numerical Mathematics. 2014. DOI : 10.1007/s10543-013-0455-z.Bivariate Matrix Functions
Operators And Matrices. 2014. DOI : 10.7153/oam-08-23.Algorithm 941: htucker-A MATLAB Toolbox for Tensors in Hierarchical Tucker Format
ACM Transactions on Mathematical Software. 2014. DOI : 10.1145/2538688.Low-Rank Tensor Methods With Subspace Correction For Symmetric Eigenvalue Problems
SIAM Journal On Scientific Computing. 2014. DOI : 10.1137/130949919.Subspace Methods For Computing The Pseudospectral Abscissa And The Stability Radius
SIAM Journal On Matrix Analysis And Applications. 2014. DOI : 10.1137/120869432.Optimally Packed Chains of Bulges in Multishift QR Algorithms
ACM Transactions on Mathematical Software. 2014. DOI : 10.1145/2559986.Robust Solution Methods for Nonlinear Eigenvalue Problems
Lausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5920.A preconditioned low-rank CG method for parameter-dependent Lyapunov matrix equations
Numerical Linear Algebra With Applications. 2013. DOI : 10.1002/nla.1919.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.An Error Analysis Of Galerkin Projection Methods For Linear Systems With Tensor Product Structure
SIAM Journal On Numerical Analysis. 2013. DOI : 10.1137/120900204.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.Linearization techniques for band structure calculations in absorbing photonic crystals
International Journal for Numerical Methods in Engineering. 2011. DOI : 10.1002/nme.3235.Preconditioned Low-Rank Methods for High-Dimensional Elliptic PDE Eigenvalue Problems
Computational Methods in Applied Mathematics. 2011. DOI : 10.2478/cmam-2011-0020.Bivariate matrix functions
2011Linear dimension reduction for evolutionary data
2011Sparsity-seeking fusion of digital elevation models
2011Low-rank tensor Krylov subspace methods for parametrized linear systems
SIAM Journal on Matrix Analysis and Applications. 2011. DOI : 10.1137/100799010.Optimal similarity registration of volumentric images
2011A 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.Continuation of eigenvalues and invariant pairs for parameterized nonlinear eigenvalue problems
Numerische Mathematik. 2011. DOI : 10.1007/s00211-011-0392-1.Continuation of eigenvalues and invariant pairs for parameterized nonlinear eigenvalue problems
2011Perturbation, extraction and refinement of invariant pairs for matrix polynomials
Linear Algebra and its Applications. 2011. DOI : 10.1016/j.laa.2010.06.029.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.Optimal image alignment with random projections of manifolds: algorithm and geometric analysis
IEEE Transactions on Image Processing. 2011. DOI : 10.1109/TIP.2010.2102044.The SLICOT Toolboxes for Matlab | Die SLICOT-Toolboxen für Matlab
At-Automatisierungstechnik. 2010. DOI : 10.1524/auto.2010.0814.On the computation of structured singular values and pseudospectra
Systems and Control Letters. 2010. DOI : 10.1016/j.sysconle.2009.12.007.A Novel Parallel QR Algorithm For Hybrid Distributed Memory HPC Systems
SIAM Journal On Scientific Computing. 2010. DOI : 10.1137/090756934.Reduction to Condensed Forms for Symmetric Eigenvalue Problems on Multi-core Architectures
2010. 8th International Conference on Parallel Processing and Applied Mathematics, Wroclaw, POLAND, Sep 13-16, 2009. p. 387 - 395. DOI : 10.1007/978-3-642-14390-8_40.On the curvature of pattern transformation manifolds: Numerical estimation and applications
2010. AAAI Symposium on Manifold Learning.Krylov subspace methods for linear systems with tensor product structure
SIAM Journal on Matrix Analysis and Applications. 2009. DOI : 10.1137/090756843.Parallel eigenvalue reordering in real Schur forms
Concurrency And Computation-Practice & Experience. 2009. DOI : 10.1002/cpe.1386.On the structured distance to uncontrollability
Systems and Control Letters. 2009. DOI : 10.1016/j.sysconle.2008.09.005.Structured Hölder condition numbers for multiple eigenvalues
SIAM Journal on Matrix Analysis and Applications. 2009. DOI : 10.1137/060672893.A block Newton method for nonlinear eigenvalue problems
Numerische Mathematik. 2009. DOI : 10.1007/s00211-009-0259-x.Implicit QR algorithms for palindromic and even eigenvalue problems
Numerical Algorithms. 2009. DOI : 10.1007/s11075-008-9226-3.Optimal image alignment with random measurements
2009. European Signal Processing Conference (EUSIPCO).Enseignement & Phd
Enseignement
Mathematics
Doctorants
Guido Margherita, He Haoze, Lam Hei Yin, Matti Fabio, Oehme Peter Bernd, Shao Nian,A dirigé les thèses EPFL de
Cortinovis Alice , Effenberger Peter Cedric , Persson Ulf David , 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
Advanced numerical analysis I
Construction et analyse de méthodes numériques pour la solution de problèmes d'approximation, d'algèbre linéaire et d'analyse.
Numerical analysis
Le cours présente des méthodes numériques pour la résolution de problèmes mathématiques comme des systèmes d'équations linéaires ou non linéaires, approximation de fonctions, intégration et dérivation, équations différentielles
Randomized matrix computations
Ce cours traite des algorithmes aléatoires qui ont été développés au cours de la dernière décennie pour résoudre des problèmes d'algèbre linéaire à grande échelle, par exemple dans le calcul scientifique et l'apprentissage statistique. On s'occupe du développement et de l'analyse de tels algorithmes
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.