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Recently, Y.Q. Bai, M. El Ghami and C. Roos [3]
introduced a new class of
so-called eligible kernel functions which are defined by some
simple conditions.
The authors designed primal-dual interior-point methods for linear optimization (LO)
based on eligible kernel functions
and simplified the analysis of these methods considerably.
In this paper we consider the semidefinite optimization (SDO) problem
and we generalize the aforementioned results for LO to SDO.
The iteration bounds obtained are...
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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