DocumentCode
169613
Title
A New Primal-Dual Interior-Point Algorithm for Semidefinite Optimization
Author
Lee, Yong-hoon ; Jin-Hee Jin ; Gyeong-Mi Cho
Author_Institution
Dept. of Math., Pusan Nat. Univ., Busan, South Korea
fYear
2014
fDate
6-9 May 2014
Firstpage
1
Lastpage
4
Abstract
We propose a new primal-dual interior-point algorithm for semidefinite optimization(SDO) based on an eligible barrier function. New search directions and proximity measures are proposed based on the barrier function. We show that the algorithm has O(√n log(n/ε)) and O(√n(log n)log(n/ε)) complexity results for small- and large-update methods, respectively. These are the best known complexity results for such methods.
Keywords
computational complexity; optimisation; search problems; SDO; complexity results; eligible barrier function; large-update methods; primal-dual interior-point algorithm; proximity measures; search directions; semidefinite optimization; small-update methods; Complexity theory; Educational institutions; Kernel; Optimization; Symmetric matrices; Tin;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Applications (ICISA), 2014 International Conference on
Conference_Location
Seoul
Print_ISBN
978-1-4799-4443-9
Type
conf
DOI
10.1109/ICISA.2014.6847334
Filename
6847334
Link To Document