Numerical Algorithms and High-Performance Computing - CADMOS Chair

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Page 14 of 34

Fundamental Group And Pluridifferentials On Compact Kahler Manifolds

Yohan Cédric Loup Brunebarbe

A compact Kahler manifold X is shown to be simply connected if its 'symmetric cotangent algebra' is trivial. Conjecturally, such a manifold should even be rationally connected. The relative version is also shown: a proper surjective connected holomorphic m ...

Independent Univ Moscow-Ium2016

MATHICSE Technical Report : Preconditioned low-rank Riemannian optimization for linear systems with tensor product structure

Daniel Kressner, Michael Maximilian Steinlechner

The numerical solution of partial differential equations on high-dimensional domains gives rise to computationally challenging linear systems. When using standard discretization techniques, the size of the linear system grows exponentially with the number ...

MATHICSE2015

MATHICSE Technical Report : Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Fabio Nobile, Lorenzo Tamellini

We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation with random data, where the random coefficient is parametrized by means of a countable sequence of terms in a ...

MATHICSE2015

Truncated low-rank methods for solving general linear matrix equations

Daniel Kressner, Petar Sirkovic

This work is concerned with the numerical solution of large-scale linear matrix equations A1XB1T++AKXBKT=C. The most straightforward approach computes XRmxn from the solution of an mn x mn linear system, typically limiting the feasible values of m,n to a f ...

Wiley-Blackwell2015

Low-rank tensor approximation for high-order correlation functions of Gaussian random fields

Fabio Nobile, Daniel Kressner, Christine Tobler, Rajesh Kumar

Gaussian random fields are widely used as building blocks for modeling stochastic processes. This paper is concerned with the efficient representation of d-point correlations for such fields, which in turn enables the representation of more general stochas ...

2015

MATHICSE Technical Report : Accelerated filtering on graphs using Lanczos method

Pierre Vandergheynst, Daniel Kressner, Nathanaël Perraudin, Ana Susnjara

Sinal-processing on graphs has developed into a very active field of research during the last decade. In particular, the number of applications using frames con-structed from graphs, like wavelets on graphs, has substantially increased. To attain scalabili ...

MATHICSE2015

MATHICSE Technical Report: Reduced basis methods: from low-rank matrices to low-rank tensor

Daniel Kressner, Jonas Ballani

We propose a novel combination of the reduced basis method with low-rank tensor techniques for the efficient solution of parameter-dependent linear systems in the case of several parameters. This combination, called rb Tensor, consists of three ingredients ...

MATHICSE2015

MATHICSE Technical Report : Subspace acceleration for large-scale parameter-dependent Hermitian eigenproblems

Daniel Kressner, Petar Sirkovic

This work is concerned with approximating the smallest eigenvalue of a parameter-dependent Hermitian matrix A(μ) for many parameter values μ ∈ RP. The design of reliable and efficient algorithms for addressing this task is of importance in a variety of app ...

MATHICSE2015

MATHICSE Technical Report : A theoretical study of COmpRessed SolvING for advection-diffusion-reaction problems

Fabio Nobile, Simone Brugiapaglia

We present a theoretical analysis of the CORSING (COmpRessed SolvING) method for the numerical approximation of partial differential equations based on compressed sensing. In particular, we show that the best s-term approximation of the weak solution of a ...

MATHICSE2015

An Adjoint Approach for Stabilizing the Parareal Method

Jan Sickmann Hesthaven, Allan Svejstrup Nielsen, Yvon Maday

The parareal algorithm seeks to extract parallelism in the time-integration direction of time-dependent differential equations. While it has been applied with success to a wide range of problems, it suffers from some stability issues when applied to non-di ...

2015

MATHICSE Technical Report : Preconditioned low-rank Riemannian optimization for linear systems with tensor product structure

Daniel Kressner, Michael Maximilian Steinlechner

MATHICSE2015

MATHICSE Technical Report : Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity

Fabio Nobile, Lorenzo Tamellini

MATHICSE2015