A primal-dual interior point algorithm for convex quadratic programming based on a new parametric kernel function. (3rd August 2021)

• Record Type: Journal Article
• Authors: Boudjellal, N.; Roumili, H.; Benterki, DJ.
• Is Part Of: Optimization. Volume 70:Number 8(2021)
• Page Start: 1703
• Page End: 1724
• Publication Date: 2021-08-03
• Subjects: Convex quadratic programming -- interior point methods -- kernel function; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2020.1751156
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract : In this paper, we deal with a polynomial primal-dual interior-point algorithm for solving convex quadratic programming based on a new parametric kernel function with an exponential barrier term. The proposed kernel function is not logarithmic and not self-regular. We analyze a class of large and small-update versions which are based on our new kernel function. The complexity obtained generalizes the result given by Bai et al. This result is the first to reach this goal. Finally, some numerical results are provided to show the efficiency of the proposed algorithm and to compare it with an available method.