Daniel Kressner

Professor

École Polytechnique Fédérale de Lausanne

Switzerland

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About

Daniel Kressner is a Professor at École Polytechnique Fédérale de Lausanne in Switzerland. His research areas encompass low-rank tensor networks, randomized numerical methods, and optimization techniques related to matrix equations. Recent publications include works on tensor network operators, low-rank approximations, and nonlinear eigenvalue problems.

Articles (23)

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2021

Collaborators (5)

STEFANO MASSEI

Assistant professor (RTDb)

University of Pisa

ITALY

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Stefan Güttel

The University of Manchester

UNITED KINGDOM

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Nicolas Boullé

Assistant Professor

Imperial College London

UNITED KINGDOM

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Davide Palitta

Junior Assistant Professor

University of Bologna

ITALY

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Bart Vandereycken

Université de Genève

SWITZERLAND

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Daniel Kressner

Research Interests

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About

Articles (23)

Randomized Nyström Approximation of Non-negative Self-Adjoint Operators

Low-Rank Tree Tensor Network Operators for Long-Range Pairwise Interactions

Randomized Low-Rank Runge–Kutta Methods

Preconditioned Low-Rank Riemannian Optimization for Symmetric Positive Definite Linear Matrix Equations

Randomized Sketching of Nonlinear Eigenvalue Problems

From low-rank retractions to dynamical low-rank approximation and back

Solving a class of infinite‐dimensional tensor eigenvalue problems by translational invariant tensor ring approximations

Randomized low‐rank approximation of parameter‐dependent matrices

Approximation in the extended functional tensor train format

Randomized Joint Diagonalization of Symmetric Matrices

Speeding Up Krylov Subspace Methods for Computing \(\boldsymbol{{f}(A){b}}\) via Randomization

Computing Functions of Symmetric Hierarchically Semiseparable Matrices

Randomized Low-Rank Approximation of Monotone Matrix Functions

Low-Rank Tensor Approximations for Solving Multimarginal Optimal Transport Problems

Streaming Tensor Train Approximation

Hierarchical adaptive low‐rank format with applications to discretized partial differential equations

Improved Variants of the Hutch++ Algorithm for Trace Estimation

Continuation Methods for Riemannian Optimization

Divide-and-Conquer Methods for Functions of Matrices with Banded or Hierarchical Low-Rank Structure

Compress‐and‐restart block Krylov subspace methods for Sylvester matrix equations

A fast spectral divide‐and‐conquer method for banded matrices

Functional Tucker Approximation Using Chebyshev Interpolation

Low-Rank Updates of Matrix Functions II: Rational Krylov Methods

Collaborators (5)

STEFANO MASSEI

Stefan Güttel

Nicolas Boullé

Davide Palitta

Bart Vandereycken

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