Publications related to Tensor product

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

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 : Riemannian optimization for high-dimensional tensor completion

Michael Maximilian Steinlechner

Tensor completion aims to reconstruct a high-dimensional data set with a large fraction of missing entries. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem restricte ...

MATHICSE2015

Low-rank tensor completion by Riemannian optimization

Daniel Kressner, Michael Maximilian Steinlechner

In tensor completion, the goal is to fill in missing entries of a partially known tensor under a low-rank constraint. We propose a new algorithm that performs Riemannian optimization techniques on the manifold of tensors of fixed multilinear rank. More spe ...

Springer2014

Algorithm 941: htucker-A MATLAB Toolbox for Tensors in Hierarchical Tucker Format

Daniel Kressner, Christine Tobler

The hierarchical Tucker format is a storage-efficient scheme to approximate and represent tensors of possibly high order. This article presents a MATLAB toolbox, along with the underlying methodology and algorithms, which provides a convenient way to work ...

Assoc Computing Machinery2014

Tensor optimisation for optical-interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Anna Auria Rasclosa

Image recovery in optical interferometry is an ill-posed nonlinear inverse problem arising from incomplete power spectrum and bispectrum measurements. We reformulate this nonlin- ear problem as a linear problem for the supersymmetric rank-1 order-3 tensor ...

Oxford Univ Press2014

MATHICSE Technical Report : 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 ...

MATHICSE2014

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Michaël Unser, Emrah Bostan, Stamatios Lefkimmiatis, Emmanuel Augustin Julien Froustey

We develop a variational algorithm for reconstructing phase objects from a series of bright field micrographs. Our mathematical model is based on the transport-of-intensity equation (TIE), which links the phase of a complex field to the axial derivative of ...

Ieee2014

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Michaël Unser, Emrah Bostan, Stamatios Lefkimmiatis, Emmanuel Augustin Julien Froustey

IEEE2014

Reduction and Abstraction Techniques for BIP

Simon Bliudze, Mohammad Noureddine

Reduction and abstraction techniques have been proposed to address the state space explosion problem in verification. In this paper, we present reduction and abstraction techniques for component-based systems modeled in BIP (Behavior, Interaction and Prior ...

Springer2014

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Tensor product

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

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

MATHICSE Technical Report : Riemannian optimization for high-dimensional tensor completion

Low-rank tensor completion by Riemannian optimization

Algorithm 941: htucker-A MATLAB Toolbox for Tensors in Hierarchical Tucker Format

Tensor optimisation for optical-interferometric imaging

MATHICSE Technical Report : Low-rank tensor approximation for high-order correlation functions of Gaussian random fields

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Reduction and Abstraction Techniques for BIP

Graph Chatbot

Chat with Graph Search

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

Algorithm 941: htucker-A MATLAB Toolbox for Tensors in Hierarchical Tucker Format

Tensor optimisation for optical-interferometric imaging

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

Digital Phase Reconstruction via Iterative Solutions of Transport-of-Intensity Equation

MATHICSE Technical Report : Low-rank tensor approximation for high-order correlation functions of Gaussian random fields

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

MATHICSE Technical Report : Riemannian optimization for high-dimensional tensor completion

Low-rank tensor completion by Riemannian optimization

Reduction and Abstraction Techniques for BIP