New parameterized kernel functions for linear optimization

DOI10.1007/s10898-012-9934-zzbMath1254.90299OpenAlexW2004402240MaRDI QIDQ693134

Jing Zhang, Wei Xie, Yan-Qin Bai

Publication date: 7 December 2012

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-012-9934-z

zbMATH Keywords

linear programminglarge-update methodsprimal-dual interior-point method

Mathematics Subject Classification ID

Interior-point methods (90C51) Renewal theory (60K05)

Related Items (4)

A new interior-point algorithm for \(P_{\ast}(k)\)-NCP based on a class of parametric kernel functions ⋮ The new full-Newton step interior-point algorithm for the Fisher market equilibrium problems based on a kernel function ⋮ A new primal-dual interior-point method for semidefinite optimization based on a parameterized kernel function ⋮ An efficient twice parameterized trigonometric kernel function for linear optimization

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 PRIMAL-DUAL INTERIOR-POINT ALGORITHM BASED ON A NEW KERNEL FUNCTION
• A New Efficient Large-Update Primal-Dual Interior-Point Method Based on a Finite Barrier
• A Comparative Study of Kernel Functions for Primal-Dual Interior-Point Algorithms in Linear Optimization
• Nonlinear Programming

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