首页|A Full-Newton Step Feasible IPM for Semidefinite Optimization Based on a Kernel Function with Linear Growth Term

A Full-Newton Step Feasible IPM for Semidefinite Optimization Based on a Kernel Function with Linear Growth Term

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In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth term.The kernel function is used both for determining the search directions and for measuring the distance between the given iterate and the μ-cen-ter for the algorithm.By developing a new norm-based proximity measure and some technical results,we derive the iteration bound that coincides with the currently best known iteration bound for the algorithm with small-update method.In our knowledge,this result is the first instance of full-Newton step feasible inte-rior-point method for SDO which involving the kernel function.

semidefinite optimizationinteriorpoint algorithmkernel functioniteration complexity

GENG Jie、ZHANG Mingwang、PANG Jinjuan

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Mathematics Department,Anhui Institute of Information Technology,Wuhu 241000,Anhui,China

College of Science,China Three Gorges University,Yichang 443002,Hubei,China

Supported by University Science Research Project of Anhui Provinceand University Teaching Research Project of Anhui Province

KJ2019A12972019jxtd144

2020

10.19823/j.cnki.1007-1202.2020.0065

武汉大学自然科学学报(英文版)
武汉大学

武汉大学自然科学学报(英文版)

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影响因子：0.066
ISSN：1007-1202
年,卷(期)：2020.25(6)
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