Guoqiang WangLingcheng Chen KongJiyuan TaoGoran Lešaja2025-10-06201500223239, 157328780022-32391573-287810.1007/s10957-014-0696-2https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937939954&doi=10.1007%2Fs10957-014-0696-2&partnerID=40&md5=3bc0ff52eaf46ad16ae5bf04578d7878https://gcris.yasar.edu.tr/handle/123456789/9889In this paper an improved complexity analysis of full Nesterov–Todd step feasible interior-point method for symmetric optimization is considered. Specifically we establish a sharper quadratic convergence result using several new results from Euclidean Jordan algebras which leads to a wider quadratic convergence neighbourhood of the central path for the iterates in the algorithm. Furthermore we derive the currently best known iteration bound for full Nesterov–Todd step feasible interior-point method. © 2022 Elsevier B.V. All rights reserved.EnglishEuclidean Jordan Algebras, Full Nesterov–todd Step, Interior-point Methods, Linear Optimization Over Symmetric Cones, Polynomial Complexity, Algebra, Iterative Methods, Complexity Analysis, Euclidean Jordan Algebra, Full Nesterov–todd Step, Interior-point Method, Linear Optimization, Linear Optimization Over Symmetric Cone, Polynomial Complexity, Quadratic Convergence, Symmetric Cone, Symmetric Optimizations, Linear ProgrammingAlgebra, Iterative methods, Complexity analysis, Euclidean Jordan algebra, Full nesterov–todd step, Interior-point method, Linear optimization, Linear optimization over symmetric cone, Polynomial complexity, Quadratic convergence, Symmetric cone, Symmetric optimizations, Linear programmingArticle