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A Constraint-reduced Algorithm for Semidefinite Optimization Problems using HKM and AHO directions

Sungwoo Park (swpark81***at***gmail.com)

Abstract: We develop a new constraint-reduced infeasible predictor-corrector interior point method for semidefinite programming, and we prove that it has polynomial global convergence and superlinear local convergence. While the new algorithm uses HKM direction in predictor step, it adopts AHO direction in corrector step to obtain faster approach to the central path. In contrast to the previous constraint-reduced algorithm, the proposed algorithm can accomplish superlinear convergence without repeated corrector step due to the fast centering effect of AHO direction.

Keywords: Semidefinite programming, Interior point methods, Constraint reduction, Primal dual infeasible, Global and Local convergence

Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming )

Citation:

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Entry Submitted: 05/23/2015
Entry Accepted: 05/23/2015
Entry Last Modified: 06/02/2015

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