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|Open}}
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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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fixed-rank matrices  to two well-studied manifolds: the Grassmann manifold of linear subspaces and the cone
 
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  
 
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
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low-rank Kalman filtering,  linear regression with low-rank 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
  • 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.