Difference of convex functions for eigenvalue problems

Series: 
Applied and Computational Mathematics Seminar
Monday, August 8, 2016 - 14:00
1 hour (actually 50 minutes)
Location: 
Skiles 006
,  
UNIST, Korea
Organizer: 
Inspired by the usefulness of difference of convex functions in some problems, e.g. sparse representations, we use such an idea of difference of convex functions to propose a method of finding an eigenfunction of a self-adjointoperator.  In a matrix setting, this method always finds an eigenvector of a symmetric matrix corresponding to the smallest eigenvalue without solving Ax=b. In fact, such a matrix A is allowed to be singular, as well. We can apply the same setting to a generalized eigenvalue problem. We will discuss its convergence as well.