A Polynomial-time Interior-point Algorithm for Convex Quadratic Semidefinite Optimization

In this paper we propose a primal-dual interior-point algorithm for convex quadratic semidefinite optimization problem. The search direction of algorithm is defined in terms of a matrix function and the iteration is generated by full-Newton step. Furthermore, we derive the iteration bound for the algorithm with small-update method, namely, O($\sqrt{n}$ log $\frac{n}{\varepsilon}$), which is best-known bound so far