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UID:1852@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20170703T140000
DTEND;TZID=Europe/Paris:20170703T150000
DTSTAMP:20170618T120000Z
URL:https://www.i2m.univ-amu.fr/evenements/globally-convergent-jacobi-type
 -algorithms-for-symmetric-tensor-diagonalization/
SUMMARY: (...): Globally convergent Jacobi-type algorithms for symmetric te
 nsor diagonalization
DESCRIPTION:: Symmetric tensors (or sets of symmetric matrices)\, in genera
 l\, cannot be diagonalized (jointly diagonalized). Motivated by applicatio
 ns in signal processing and machine learning\, we consider the problem of 
 approximate diagonalization by orthogonal transformations. For the Jacobi-
 type algorithm of [SIAM J. Matrix Anal. Appl.\, 2(34):651–672\, 2013]\, 
 we prove its global convergence in the case of simultaneous orthogonal dia
 gonalizationof symmetric matrices or 3rd-order tensors. We also propose a 
 new proximal Jacobi-type algorithm and prove its global convergence for a 
 wide range of tensor approximation problems.This is joint work with Jianze
  Li (Tianjin University) and Pierre Comon (GIPSA-lab\, CNRS and Univ. Gren
 oble Alpes).This work was supported by the ERC project ‘DECODA’ no.320
 594\, in the frame of the European program FP7/2007-2013. Jianze Li was pa
 rtially supported by the National Natural Science Foundation of China (No.
 11601371). http://www.gipsa-lab.grenoble-inp.fr/~konstantin.usevich/
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