What is it about?
This is a study on how a second order method, an interior point method, behaves locally when solving semidefinite linear complementarity problems, an important class of matrix optimization problems that has wide applicability in diverse areas.
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Why is it important?
The study of good local convergence behavior is important in applications where solutions to optimization problems need to be obtained as exactly as possible and in as quickly a way as possible. This paper contributes to this study by considering the local convergence behavior of a second order optimization algorithm to solve a class of matrix optimization problems.
Perspectives
This work is part of a series of papers done by the author on the local convergence study of interior point methods on semidefinite problems.
Dr Chee Khian Sim
University of Portsmouth
Read the Original
This page is a summary of: Superlinear Convergence of an Infeasible Predictor-Corrector Path-Following Interior Point Algorithm for a Semidefinite Linear Complementarity Problem Using the Helmberg–Kojima–Monteiro Direction, SIAM Journal on Optimization, January 2011, Society for Industrial & Applied Mathematics (SIAM),
DOI: 10.1137/090779279.
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