A Polynomial‐time Interior‐point Algorithm for Convex QuadraticSemidefinite 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( $$O\left(\sqrt{n}{\log }_{ϵ}^n\right)$$ ), which is best‐known bound so far.