Seminars and Colloquia by Series

Thursday, October 19, 2017 - 15:05 , Location: Skiles 006 , Yao Xie , ISyE, Georgia Institute of Technology , Organizer: Mayya Zhilova
Thursday, October 19, 2017 - 15:05 , Location: Skiles 006 , Yao Xie , ISyE, Georgia Institute of Technology , Organizer: Mayya Zhilova
Wednesday, October 18, 2017 - 14:05 , Location: Skiles 005 , Alex Yosevich , University of Rochester , Organizer: Shahaf Nitzan
Wednesday, October 18, 2017 - 14:05 , Location: Skiles 005 , Alex Yosevich , University of Rochester , Organizer: Shahaf Nitzan
Monday, October 16, 2017 - 14:00 , Location: Skiles 005 , Dr. Barak Sober , Tel Aviv University , barakino@gmail.com , Organizer: Doron Lubinsky
We approximate a function defined over a *d*-dimensional manifold *M* ⊂*Rn* utilizing only noisy function values at noisy locations on the manifold. To produce the approximation we do not require any knowledge regarding the manifold other than its dimension *d*. The approximation scheme is based upon the Manifold Moving Least-Squares (MMLS) and is therefore resistant to noise in the domain *M* as well. Furthermore, the approximant is shown to be smooth and of approximation order of *O(hm+1) *for non-noisy data, where *h* is the mesh size w.r.t *M,* and *m* is the degree of the local polynomial approximation. In addition, the proposed algorithm is linear in time with respect to the ambient space dimension *n*, making it useful for cases where *d<
Monday, October 16, 2017 - 14:00 , Location: Skiles 005 , Dr. Barak Sober , Tel Aviv University , barakino@gmail.com , Organizer: Doron Lubinsky
We approximate a function defined over a *d*-dimensional manifold *M* ⊂*Rn* utilizing only noisy function values at noisy locations on the manifold. To produce the approximation we do not require any knowledge regarding the manifold other than its dimension *d*. The approximation scheme is based upon the Manifold Moving Least-Squares (MMLS) and is therefore resistant to noise in the domain *M* as well. Furthermore, the approximant is shown to be smooth and of approximation order of *O(hm+1) *for non-noisy data, where *h* is the mesh size w.r.t *M,* and *m* is the degree of the local polynomial approximation. In addition, the proposed algorithm is linear in time with respect to the ambient space dimension *n*, making it useful for cases where *d<
Monday, October 16, 2017 - 13:55 , Location: Skiles 006 , Kyle Hayden , Boston College , Organizer: John Etnyre
Monday, October 16, 2017 - 13:55 , Location: Skiles 006 , Kyle Hayden , Boston College , Organizer: John Etnyre
Wednesday, October 11, 2017 - 14:05 , Location: Skiles 005 , Akram Aldroubi , Vanderbilt University , Organizer: Shahaf Nitzan
Wednesday, October 11, 2017 - 14:05 , Location: Skiles 005 , Akram Aldroubi , Vanderbilt University , Organizer: Shahaf Nitzan

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