Nonlinear eigenvalue problems

Tuesday, April 26, 2016 - 1:25pm - 2:25pm
Chen-Yun Lin (University of Toronto)
Data sets often have certain nonlinear structures. Modeling/Approximating the nonlinear structures by the manifold model is attracting more and more attention in data science nowadays. A natural question is to find coordinate charts for data sets, i.e., manifolds. In this talk, I will discuss embedding of manifolds via eigen-vector fields of the connection Laplacian. For data sets, the eigen-vector fields can be computed by the graph connection Laplacian (GCL). I will also discuss the mathematical framework of image denoising via the GCL.
Tuesday, November 2, 2010 - 10:00am - 10:45am
Yvon Maday (Université de Paris VI (Pierre et Marie Curie))
Approximation of non linear eigenvalue problems represent the key ingredient in quantum chemistry. These approximation are much computer demanding and these approximations saturate the ressources of many HPC centers. Being nonlinear, the approximation methods are iterative and a way to reduce the cost is to use different grids as has been proposed in fluid mechanics for various non linear problems as the Navier Stokes problem.
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