Difference between revisions of "Rodolphe Sepulchre, June 2013"
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{{agenda begin}}  {{agenda begin}}  
{{agenda item9:30aRichard Murray, 109 Steele Lab}}  {{agenda item9:30aRichard Murray, 109 Steele Lab}}  
−  {{agenda item  +  {{agenda item9:45aMeet with Richard's NCS group, 110 Steele}} 
−  +  * 9:4510:45: Necmiye, Mumu, Eric, Rangoli  
−  +  * 10:4511:45: Enoch, Marcella, Anandh, Dan  
−  {{agenda item  +  {{agenda item11:45aLunch with Venkat, CMS faculty}} 
−  {{agenda item  +  {{agenda item1:15pSeminar setup}} 
−  {{agenda item  +  {{agenda item1:30pSeminar, 121 ANB}} 
−  {{agenda item3:  +  {{agenda item3:00pVenkat Chandrasekaran, 300 ANB}} 
−  {{agenda item  +  {{agenda item3:45pLijun Chen, 202 ANB}} 
−  {{agenda item  +  {{agenda item4:30pDone, Meet Caltech car in transportation lot, just East of Steele Lab}} 
{{agenda end}}  {{agenda end}}  
=== Abstract ===  === Abstract ===  
+  
+  <center>  
+  '''The geometry of (thin) SVD revisited for largescale computations'''  
+  
+  Rodolphe Sepulchre<br>  
+  University of Liege, Belgium  
+  </center>  
+  
+  The talk will introduce a riemannian framework for largescale computations over the  
+  set of lowrank matrices. The foundation is geometric and the motivation  
+  is algorithmic, with a bias towards efficient computations in largescale problems.  
+  We will explore how classical matrix factorizations connect the riemannian geometry of the set of  
+  fixedrank matrices to two wellstudied manifolds: the Grassmann manifold of linear subspaces and the cone  
+  of positive definite matrices. The theory will be illustrated on various applications, including  
+  lowrank Kalman filtering, linear regression with lowrank priors, matrix completion, and the choice of a suitable metric for Diffusion Tensor Imaging. 
Latest revision as of 16:49, 3 June 2013
Rodolphe Sepulchre will visit Caltech on 3 June 2013 (Mon).
Agenda
9:30a  Richard Murray, 109 Steele Lab 
9:45a  Meet with Richard's NCS group, 110 Steele

11:45a  Lunch with Venkat, CMS faculty 
1:15p  Seminar setup 
1:30p  Seminar, 121 ANB 
3:00p  Venkat Chandrasekaran, 300 ANB 
3:45p  Lijun Chen, 202 ANB 
4:30p  Done, Meet Caltech car in transportation lot, just East of Steele Lab 
Abstract
The geometry of (thin) SVD revisited for largescale computations
Rodolphe Sepulchre
University of Liege, Belgium
The talk will introduce a riemannian framework for largescale computations over the set of lowrank matrices. The foundation is geometric and the motivation is algorithmic, with a bias towards efficient computations in largescale problems. We will explore how classical matrix factorizations connect the riemannian geometry of the set of fixedrank matrices to two wellstudied manifolds: the Grassmann manifold of linear subspaces and the cone of positive definite matrices. The theory will be illustrated on various applications, including lowrank Kalman filtering, linear regression with lowrank priors, matrix completion, and the choice of a suitable metric for Diffusion Tensor Imaging.