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

Authors

  • Nawel Boudjellal Ferhat Abbas Setif University
  • Hayet Roumili Ferhat Abbas Setif University
  • Djamel Benterki Ferhat Abbas Setif University

DOI:

https://doi.org/10.5269/bspm.47772

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 signicantly the so far obtained complexity results. This
result is the rst to reach this goal.

References

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Published

2022-02-02

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Research Articles