BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Optimization with rank-structured matrices
DTSTART:20241119T161500
DTEND:20241119T171500
DTSTAMP:20261012T052033Z
UID:244a94817061bc2edf1fb881f0ef8a53c86f1370e3fcba35d7ec52fa
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Martin Skovgaard Andersen - DTU Orbit\, Denmark\nComputa
 tional Mathematics Seminar \n\nAbstract :\nSimple optimization methods com
 monly suffer from slow convergence near a minimizer\, and yet such methods
  are often a de facto choice when dealing with large-scale problems with a
  large number of variables and/or large quantities of data. This talk pres
 ents some examples of how this issue can be addressed using rank-structure
 d matrices as building blocks. Such matrices have a low-rank structure tha
 t admits a storage-efficient representation and fast algebraic operations 
 (e.g.\, matrix-vector multiplication\, factorization\, etc.)\, and they ar
 ise frequently in engineering and data science\, e.g.\, in applications th
 at involve kernel functions. Examples include semiseparable (SS)\, hierarc
 hical off-diagonal low rank (HODLR)\, and hierarchical semiseparable (HSS)
  matrices\, all of which allow for linear or quasi-linear time and space c
 omplexity in key operations.\n 
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR