A new parameterized kernel function for LO yielding the best known iteration bound for a large-update interior point algorithm

DOI10.1007/S13370-015-0363-2zbMath1378.90089OpenAlexW850977688MaRDI QIDQ1689710

Mohamed Achache

Publication date: 17 January 2018

Published in: Afrika Matematika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13370-015-0363-2

zbMATH Keywords

kernel functionproximitylinear optimizationcomplexity analysislarge-update primal-dual algorithm

Mathematics Subject Classification ID

Linear programming (90C05) Interior-point methods (90C51)

Related Items (3)

Complexity analysis and numerical implementation of large-update interior-point methods for SDLCP based on a new parametric barrier kernel function ⋮ An interior-point algorithm for semidefinite least-squares problems. ⋮ Theoretical and Numerical Result for Linear Optimization Problem Based on a New Kernel Function

Cites Work

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• Self-regular functions and new search directions for linear and semidefinite optimization
• A new kernel function yielding the best known iteration bounds for primal-dual interior-point algorithms
• A new proximity function generating the best known iteration bounds for both large-update and small-update interior-point methods
• A Comparative Study of Kernel Functions for Primal-Dual Interior-Point Algorithms in Linear Optimization
• A new primal-dual interior-point algorithm for convex quadratic optimization

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