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Received January 25, 2019; Accepted June 17, 2019; Published June 28, 2019
Abstract. The purpose of this paper is to improve the complexity of a large-update primal-dual interior point method for a semidefinite programming problem. We define a proximity function for the semidefinite programming problem based on a new parametric kernel function and prove that the worst-case iteration bound for the new correspondent algorithm is where .
How to Cite this Article:
Imene Touil, Djamel Benterki, A primal-dual interior-point method for the semidefinite programming problem based on a new kernel function, Journal of Nonlinear Functional Analysis, Vol. 2019 (2019), Article ID 25, pp. 1-17.