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Sayadi Shahraki, M., Mansouri, H. and Zangiabadi, M. (2017) Two Wide Neighborhood Interior-Point Methods for Symmetric Cone Optimization. Computational Optimization and Applications, 68, 29-55.
https://doi.org/10.1007/s10589-017-9905-x

has been cited by the following article:

• 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.