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Title: | Complexity Analysis of Interior Point Methods for Convex Quadratic Programming Based on a Parameterized Kernel Function |
Authors: | Boudjellal, Nawel Roumili, Hayet Benterki, Djamel |
Keywords: | Convex quadratic programming Interior point methods Kernel function Iteration bound. |
Issue Date: | 2022 |
Publisher: | Université Akli Mohend Oulhadj Bouira |
Citation: | Université Akli Mohend Oulhadj Bouira |
Abstract: | abstract: The kernel functions play an important role in the amelioration of the computational complexity of algorithms. In this paper, we present a primal-dual interior-point algorithm for solving convex quadratic programming based on a new parametric kernel function. The proposed kernel function is not logarithmic and not self-regular. We analysis a large and small-update versions which are based on a new kernel function. We obtain the best known iteration bound for large-update methods, which improves significantly the so far obtained complexity results. This result is the first to reach this goal. |
URI: | http://dspace.univ-bouira.dz:8080/jspui/handle/123456789/16555 |
Appears in Collections: | Articles |
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File | Description | Size | Format | |
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Complexity_analysis_of_interior_point_methods_for_.pdf | 246,61 kB | Unknown | View/Open |
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