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Mehrotra-Type Predictor-Corrector Algorithms for Symmetric Cone Programming in a Wide Neighborhood of the Central Path
Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord, Iran
Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
- 1 Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord, Iran
- 2 Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
American Journal of Operations Research·Volume 16 (2026)·Pages 119–139·Published 31 May 2026·DOI10.4236/ajor.2026.163006
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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.
KeywordsInterior-Point AlgorithmWide NeighborhoodMehrotra-Type AlgorithmSymmetric Cone ProgrammingEuclidean Jordan Algebra
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