Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/15240
Title: Smoothing approximations for two classes of convex eigenvalue optimization problems
Authors: YU QI
Keywords: Smoothing function, Lipschitz constant, Smoothing algorithm, Eigenvalue problems, Sigularvalue, spectral function
Issue Date: 23-Mar-2006
Source: YU QI (2006-03-23). Smoothing approximations for two classes of convex eigenvalue optimization problems. ScholarBank@NUS Repository.
Abstract: In this thesis, we consider two problems: minimizing the sum of the I? largest eigenvalues and the sum of the I? largest absolute values of eigenvalues of a parametric linear operator. In order to apply Nesterova??s smoothing algorithm to solve these two problems, we construct two computable smoothing functions whose gradients are Lipschitz continuous. This construction is based on Shia??s thesis [12] and new techniques introduced in this thesis. Numerical results on the performance of Nesterova??s smooth algorithm are also reported.
URI: http://scholarbank.nus.edu.sg/handle/10635/15240
Appears in Collections:Master's Theses (Open)

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