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In this paper a primal-dual interior-point algorithm for convex quadratic optimization based on a parametric kernel function, with parameters p isin [0,1] and q ges 1, is presented. The proposed parametric kernel function is of excellent properties which are used both for determining the search directions and measuring the distance between the given iterate and the central path for the algorithm....
In this paper a new polynomial interior-point algorithm for monotone mixed linear complementarity problem is presented. The algorithm is based on a new technique for finding a class of search directions and the strategy of the central path. At each iteration, we use only full-Newton step. Moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely,O(radic(n...
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