An interior point method with a primal-dual quadratic barrier penalty function for nonlinear semidefinite programming

作者：Kato Atsushi^*; Yabe Hiroshi; Yamashita Hiroshi

来源：Journal of Computational and Applied Mathematics, 2015, 275: 148-161.

DOI：10.1016/j.cam.2014.07.024

• Nonlinear semidefinite programming
• Primal-dual interior point method
• Primal-dual quadratic barrier penalty function
• Global convergence

In this paper, we consider an interior point method for nonlinear semidefinite programming. Yamashita, Yabe and Harada presented a primal-dual interior point method in which a nondifferentiable merit function was used. By using shifted barrier KKT conditions, we propose a differentiable primal-dual merit function within the framework of the line search strategy, and prove its global convergence property under weaker assumptions than the method of Yamashita, Yabe and Harada.

• 出版日期2015-2