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UID:pretalx-juliacon-2026-UZXK9Y@pretalx.com
DTSTART;TZID=CET:20260813T103000
DTEND;TZID=CET:20260813T110000
DESCRIPTION:We present recently developed iterative methods for approximati
 ng matrices and tensors with structural constraints such as rank and spars
 ity level and pattern\, extending to settings where data is incomplete or 
 indirectly observed\, with or without noise. These methods aim to solve ca
 nonical problems of numerical (multi-)linear algebra\, namely approximate 
 matrix inversion and low-rank matrix and tensor approximation\, when struc
 tural constraints are imposed on the approximation. While the presented re
 sults revolve around aspects of sparsity\, if time permits\, we extend the
  presentation to other structural features such as non-negativity. We also
  present how this work contributes to the ongoing development of the repos
 itories ApproximateMatrixInverses.jl\, StructuredLowRankMatrices.jl\, and 
 StructuredLowRankTensors.jl.
DTSTAMP:20260529T230840Z
LOCATION:Room 6
SUMMARY:Structured iterative approximations in numerical (multi-)linear alg
 ebra - Nicolas Venkovic
URL:https://pretalx.com/juliacon-2026/talk/UZXK9Y/
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