Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16365
Title: A Semismooth Newton-CG Augmented Lagrangian Method for Large Scale Linear and Convex Quadratic SDPS
Authors: ZHAO XINYUAN
Keywords: Convex quadratic semidefinite programming, Augmented Lagrangian, Semismoothness, Newton's method
Issue Date: 25-Nov-2009
Citation: ZHAO XINYUAN (2009-11-25). A Semismooth Newton-CG Augmented Lagrangian Method for Large Scale Linear and Convex Quadratic SDPS. ScholarBank@NUS Repository.
Abstract: In this thesis, we introduce a semismooth Newton-CG augmented Lagrangian method for solving linear and convex quadratic semidefinite programming problems from the perspective of approximate semimsooth Newton methods. Under the framework of Euclidean Jordan algebras, we study the properties of these minimization problems and analyze the convergence of our proposed method. As a special case, based on the simple structure of linear symmetric cone programming and its dual, we characterize the Lipschitz continuity of the solution mapping for the dual problem at the origin. Numerical experiments on a variety of large scale convex linear and quadratic semidefinite programming show that the proposed method is very efficient.
URI: http://scholarbank.nus.edu.sg/handle/10635/16365
Appears in Collections:Ph.D Theses (Open)

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