Complexity analysis of interior point methods for convex quadratic programming based on a parameterized Kernel function

Autor: Nawel Boudjellal, Hayet Roumili, Djamel Benterki
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
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