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A Constraint-Reduced Algorithm for Semidefinite Optimization Problems with Superlinear Convergence
Sungwoo Park (swpark81 Abstract: Constraint reduction is an essential method because the computational cost of the interior point methods can be effectively saved. Park and O'Leary proposed a constraint-reduced predictor-corrector algorithm for semidefinite programming with polynomial global convergence, but they did not show its superlinear convergence. We first develop a constraint-reduced algorithm for semidefinite programming having both polynomial global and superlinear local convergences. The new algorithm repeats a corrector step to have an iterate tangentially approach a central path, by which superlinear convergence can be achieved. Keywords: Semidefinite programming, Interior point methods, Constraint reduction, Primal dual infeasible, Local convergence Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming ) Citation: Download: [PDF] Entry Submitted: 05/21/2015 Modify/Update this entry | ||
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