Sajad Fathi-Hafshejani, Alireza Fakharzadeh J., An interior-point algorithm for semidefinite optimization based on a new parametric kernel function, Vol. 2018 (2018), Article ID 14, pp. 1-24

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DOI: 10.23952/jnfa.2018.14

Received January 6, 2018; Accepted March 26, 2018; Published April 5, 2018

Abstract. In this paper, an interior-point algorithm for Semidefinite Optimization (SDO) problems based on a new parametric kernel function is proposed. By means of some simple analysis tools, we prove that the primal-dual interior-point algorithm for solving SDO problems meets $O\left(\sqrt{n}\log{n}\log\frac{n}{\varepsilon}\right)$ , iteration complexity bound for large-update methods. Numerical results confirm that our new proposed kernel function is doing well in practice in comparison with some existing kernel functions in the literature.

How to Cite this Article:
Sajad Fathi-Hafshejani, Alireza Fakharzadeh J., An interior-point algorithm for semidefinite optimization based on a new parametric kernel function, Journal of Nonlinear Functional Analysis, Vol. 2018 (2018), Article ID 14, pp. 1-24.