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math.NA2025
Interpolatory Dynamical Low-Rank Approximation: Theoretical Foundations and Algorithms
Benjamin Carrel, Daniel Kressner, Hei Yin Lam +1
Dynamical low-rank approximation (DLRA) is a widely used paradigm for solving large-scale matrix differential equations, as they arise, for example, from the discretization of time…
math.NA2024
Randomized low-rank Runge-Kutta methods
Hei Yin Lam, Gianluca Ceruti, Daniel Kressner
This work proposes and analyzes a new class of numerical integrators for computing low-rank approximations to solutions of matrix differential equation. We combine an explicit Rung…
math.NA2024
Low-rank Tree Tensor Network Operators for Long-Range Pairwise Interactions
Gianluca Ceruti, Daniel Kressner, Dominik Sulz
Compactly representing and efficently applying linear operators are fundamental ingredients in tensor network methods for simulating quantum many-body problems and solving high-dim…