Abstract
A primal–dual interior-point method (IPM) based on a new class of proximity functions is proposed for solving Semidefinite Optimization (SDO) problems. The proposed functions are induced from the kernel functions with trigonometric barrier terms. We derive iteration complexity of large-update IPMs for SDO as O(nlognlognϵ). This improves the result obtained in Li and Zhang (2015) for linear optimization and matches to the bound for the so-called self-regular kernel functions.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
