TITLE:
Mehrotra-Type Predictor-Corrector Algorithms for Symmetric Cone Programming in a Wide Neighborhood of the Central Path
AUTHORS:
Marzieh Sayadi Shahraki, Nezam Mahdavi-Amiri
KEYWORDS:
Interior-Point Algorithm, Wide Neighborhood, Mehrotra-Type Algorithm, Symmetric Cone Programming, Euclidean Jordan Algebra
JOURNAL NAME:
American Journal of Operations Research,
Vol.16 No.3,
June
15,
2026
ABSTRACT: Two Mehrotra-type predictor-corrector interior point algorithms are proposed for solving symmetric cone optimization (SCO) problems, using the Euclidean Jordan algebra. The algorithms produce sequences of iterates in the wide neighborhood of the central path. We establish
O(
r
log
ε
?1
)
iteration complexity bound for the Nesterov-Todd (NT) scaling direction. To our knowledge, this is the best complexity result obtained so far for interior-point methods over wide neighborhood. We demonstrate the computational efficiency of the proposed algorithms by numerical test results.