Difference between revisions of "Rodolphe Sepulchre, June 2013"

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{{agenda item|9:30a|Richard Murray, 109 Steele Lab}}
 
{{agenda item|9:30a|Richard Murray, 109 Steele Lab}}
 
{{agenda item|9:45a|Meet with Richard's NCS group, 110 Steele}}
 
{{agenda item|9:45a|Meet with Richard's NCS group, 110 Steele}}
* 9:45-10:45: Necmiye, Mumu, Eric, Ivan
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* 9:45-10:45: Necmiye, Mumu, Eric, Rangoli
 
* 10:45-11:45: Enoch, Marcella, Anandh, Dan
 
* 10:45-11:45: Enoch, Marcella, Anandh, Dan
{{agenda item|11:45|Seminar setup}}
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{{agenda item|11:45a|Lunch with Venkat, CMS faculty}}
{{agenda item|12:00p|Seminar, 213 ANB}}
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{{agenda item|1:15p|Seminar setup}}
{{agenda item|1:00p|Lunch with Venkat, CMS faculty}}
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{{agenda item|1:30p|Seminar, 121 ANB}}
{{agenda item|2:15p|Venkat Chandrasekaran, 300 Annenberg}}
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{{agenda item|3:00p|Venkat Chandrasekaran, 300 ANB}}
{{agenda item|3:00p|Open}}
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{{agenda item|3:45p|Lijun Chen, 202 ANB}}
{{agenda item|3:45p|Open}}
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{{agenda item|4:30p|Done, Meet Caltech car in transportation lot, just East of Steele Lab}}
{{agenda item|4:30p|Andrea Censi)}}
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{{agenda item|5:15p|Done}}
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{{agenda end}}
 
{{agenda end}}
  

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
  • 9:45-10:45: Necmiye, Mumu, Eric, Rangoli
  • 10:45-11:45: Enoch, Marcella, Anandh, Dan
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 large-scale computations

Rodolphe Sepulchre
University of Liege, Belgium

The talk will introduce a riemannian framework for large-scale computations over the set of low-rank matrices. The foundation is geometric and the motivation is algorithmic, with a bias towards efficient computations in large-scale problems. We will explore how classical matrix factorizations connect the riemannian geometry of the set of fixed-rank matrices to two well-studied manifolds: the Grassmann manifold of linear subspaces and the cone of positive definite matrices. The theory will be illustrated on various applications, including low-rank Kalman filtering, linear regression with low-rank priors, matrix completion, and the choice of a suitable metric for Diffusion Tensor Imaging.