| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1142134 | Operations Research Letters | 2016 | 5 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Discrete Mathematics and Combinatorics
Authors
M. Reza Peyghami, S. Fathi-Hafshejani, S. Chen,