Tensors and structured matrices of low rank

Research output: Chapter in Book/Report/Conference proceedingMeeting abstract (Book)


We present a new connection between higher-order tensors and structured matrices, in the context of low-rank approximation. In particular, we show that the tensor low multilinear rank approximation problem can be reformulated as a structured matrix low-rank approximation, the latter being an extensively studied and well understood problem. For simplicity, we consider symmetric tensors. By imposing simple constraints in the optimization problem, the proposed approach is applicable to general tensors, as well as to affinely structured tensors to find (locally) best low multilinear rank approximation with the same structure.
Original languageEnglish
Title of host publicationPresentation of poster at ERNSI 2014, European Research Network on System Identification, Oostende, Belgium, September 21-24, 2014
Publication statusPublished - 21 Sep 2014
EventERNSI 2014 - Thermae Palace Hotel, Ostend, Belgium
Duration: 21 Sep 201424 Sep 2014


WorkshopERNSI 2014
OtherModelling of dynamical systems is fundamental in almost all disciplines of science and engineering, ranging from life science to plant-wide process control. Engineering uses models for the design and analysis of complex technical systems. System identification concerns the construction, estimation and validation of mathematical models of dynamical physical or engineering phenomena from experimental data. This is the 23rd version of the European Workshop on System Identification, the first one being held in Saint-Malo in 1992. All through these years the workshop has maintained the scope of bringing together European researchers in the area of System Identification, in an informal setting that gives ample opportunities for participants to meet. The workshop program is composed of lectures from invited speakers, lectures from members of the ERNSI community, and poster presentations by -particularly- the PhD students and postdocs that are active in the network.


  • low multilinear rank approximation

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