On Selberg's Eigenvalue Conjecture.
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, ( log ), which is best-known bound so far.
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