https://murray.cds.caltech.edu/api.php?action=feedcontributions&feedformat=atom&user=Venkatc 2026-05-27T12:56:04Z User contributions MediaWiki 1.44.2 https://murray.cds.caltech.edu/index.php?title=Rodolphe_Sepulchre,_June_2013&diff=16025 2013-05-31T20:07:31Z

<p>Venkatc: </p> <hr /> <div>Rodolphe Sepulchre will visit Caltech on 3 June 2013 (Mon). <br /> <br /> === Agenda ===<br /> {{agenda begin}}<br /> {{agenda item|9:30a|Richard Murray, 109 Steele Lab}}<br /> {{agenda item|9:45a|Meet with Richard's NCS group, 110 Steele}}<br /> * 9:45-10:45: Necmiye, Mumu, Eric, Rangoli<br /> * 10:45-11:45: Enoch, Marcella, Anandh, Dan<br /> {{agenda item|11:45a|Lunch with Venkat, CMS faculty}}<br /> {{agenda item|1:15p|Seminar setup}}<br /> {{agenda item|1:30p|Seminar, 121 ANB}}<br /> {{agenda item|3:00p|Venkat Chandrasekaran, 300 ANB}}<br /> {{agenda item|3:45p|Lijun Chen, 202 ANB}}<br /> {{agenda item|4:30p|Andrea Censi, 320 ANB}}<br /> {{agenda item|5:15p|Done}}<br /> {{agenda end}}<br /> <br /> === Abstract ===<br /> <br /> &lt;center&gt;<br /> '''The geometry of (thin) SVD revisited for large-scale computations'''<br /> <br /> Rodolphe Sepulchre&lt;br&gt;<br /> University of Liege, Belgium<br /> &lt;/center&gt;<br /> <br /> The talk will introduce a riemannian framework for large-scale computations over the<br /> set of low-rank matrices. The foundation is geometric and the motivation<br /> is algorithmic, with a bias towards efficient computations in large-scale problems.<br /> We will explore how classical matrix factorizations connect the riemannian geometry of the set of<br /> fixed-rank matrices to two well-studied manifolds: the Grassmann manifold of linear subspaces and the cone<br /> of positive definite matrices. The theory will be illustrated on various applications, including <br /> low-rank Kalman filtering, linear regression with low-rank priors, matrix completion, and the choice of a suitable metric for Diffusion Tensor Imaging.</div>

Venkatc https://murray.cds.caltech.edu/index.php?title=Rodolphe_Sepulchre,_June_2013&diff=16021 2013-05-30T05:18:46Z

<p>Venkatc: </p> <hr /> <div>Rodolphe Sepulchre will visit Caltech on 3 June 2013 (Mon). <br /> <br /> === Agenda ===<br /> {{agenda begin}}<br /> {{agenda item|9:30a|Richard Murray, 109 Steele Lab}}<br /> {{agenda item|9:45a|Meet with Richard's NCS group, 110 Steele}}<br /> * 9:45-10:45: Necmiye, Mumu, Eric, Rangoli<br /> * 10:45-11:45: Enoch, Marcella, Anandh, Dan<br /> {{agenda item|11:45a|Lunch with Venkat, CMS faculty}}<br /> {{agenda item|1:15p|Seminar setup}}<br /> {{agenda item|1:30p|Seminar, 213 ANB}}<br /> {{agenda item|3:00p|Open}}<br /> {{agenda item|3:45p|Open}}<br /> {{agenda item|4:30p|Andrea Censi)}}<br /> {{agenda item|5:15p|Done}}<br /> {{agenda end}}<br /> <br /> === Abstract ===<br /> <br /> &lt;center&gt;<br /> '''The geometry of (thin) SVD revisited for large-scale computations'''<br /> <br /> Rodolphe Sepulchre&lt;br&gt;<br /> University of Liege, Belgium<br /> &lt;/center&gt;<br /> <br /> The talk will introduce a riemannian framework for large-scale computations over the<br /> set of low-rank matrices. The foundation is geometric and the motivation<br /> is algorithmic, with a bias towards efficient computations in large-scale problems.<br /> We will explore how classical matrix factorizations connect the riemannian geometry of the set of<br /> fixed-rank matrices to two well-studied manifolds: the Grassmann manifold of linear subspaces and the cone<br /> of positive definite matrices. The theory will be illustrated on various applications, including <br /> low-rank Kalman filtering, linear regression with low-rank priors, matrix completion, and the choice of a suitable metric for Diffusion Tensor Imaging.</div>

Venkatc https://murray.cds.caltech.edu/index.php?title=Rodolphe_Sepulchre,_June_2013&diff=16018 2013-05-28T22:54:32Z

<p>Venkatc: </p> <hr /> <div>Rodolphe Sepulchre will visit Caltech on 3 June 2013 (Mon). <br /> <br /> === Agenda ===<br /> {{agenda begin}}<br /> {{agenda item|9:30a|Richard Murray, 109 Steele Lab}}<br /> {{agenda item|9:45a|Meet with Richard's NCS group, 110 Steele}}<br /> * 9:45-10:45: Necmiye, Mumu, Eric, Ivan<br /> * 10:45-11:45: Enoch, Marcella, Anandh, Dan<br /> {{agenda item|11:45|Seminar setup}}<br /> {{agenda item|12:00p|Seminar, 213 ANB}}<br /> {{agenda item|1:00p|Lunch with Venkat, CMS faculty}}<br /> {{agenda item|2:15p|Venkat Chandrasekaran, 300 Annenberg}}<br /> {{agenda item|3:00p|Open}}<br /> {{agenda item|3:45p|Open}}<br /> {{agenda item|4:30p|Andrea Censi)}}<br /> {{agenda item|5:15p|Done}}<br /> {{agenda end}}<br /> <br /> === Abstract ===<br /> <br /> &lt;center&gt;<br /> '''The geometry of (thin) SVD revisited for large-scale computations'''<br /> <br /> Rodolphe Sepulchre&lt;br&gt;<br /> University of Liege, Belgium<br /> &lt;/center&gt;<br /> <br /> The talk will introduce a riemannian framework for large-scale computations over the<br /> set of low-rank matrices. The foundation is geometric and the motivation<br /> is algorithmic, with a bias towards efficient computations in large-scale problems.<br /> We will explore how classical matrix factorizations connect the riemannian geometry of the set of<br /> fixed-rank matrices to two well-studied manifolds: the Grassmann manifold of linear subspaces and the cone<br /> of positive definite matrices. The theory will be illustrated on various applications, including <br /> low-rank Kalman filtering, linear regression with low-rank priors, matrix completion, and the choice of a suitable</div>

Venkatc