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In this paper, we propose a feasible primal-dual path-following algorithm for convex quadratic programs. At each interior-point iteration the algorithm uses a full-Newton step and a suitable proximity measure for tracing approximately the central path. We show that the short-step algorithm has the best known iteration bound,namely O(√ n log (n+1)).
In this paper, we present a primal-dual interior point algorithm for linearly constrained convex optimization (LCCO). The algorithm uses only full-Newton step to update iterates with an appropriate proximity measure for controlling feasible iterations near the central path during the solution process. The favorable polynomial complexity bound for the… (More)