Complexity analysis of interior point methods for convex quadratic programming based on a parameterized Kernel function
Autor: | Nawel Boudjellal, Hayet Roumili, Djamel Benterki |
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Jazyk: | English<br />Portuguese |
Rok vydání: | 2022 |
Předmět: | |
Zdroj: | Boletim da Sociedade Paranaense de Matemática, Vol 40 (2022) |
Druh dokumentu: | article |
ISSN: | 0037-8712 2175-1188 |
DOI: | 10.5269/bspm.47772 |
Popis: | 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 signicantly the so far obtained complexity results. This result is the rst to reach this goal. |
Databáze: | Directory of Open Access Journals |
Externí odkaz: |
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