An extension of the MS pivoting rule to capacitated network flow problems
Achievable potential reductions in the method of Kojima et al. in the case of linear programming
Generic Primal-dual Interior Point Methods Based on a New Kernel Function
In this paper we present a generic primal-dual interior point methods (IPMs) for linear optimization in which the search direction depends on a univariate kernel function which is also used as proximity measure in the analysis of the algorithm. The proposed kernel function does not satisfy all the conditions proposed in [2]. We show that the corresponding large-update algorithm improves the iteration complexity with a factor when compared with the method based on the use of the classical...
Inverse barrier methods for linear programming
Kernel-function Based Algorithms for Semidefinite Optimization
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...
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