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A wide neighborhood primal-dual predictor-corrector interior-point method for symmetric cone optimization

Sayadi Shahraki, M ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1007/s11075-017-0387-9
  3. Publisher: Springer New York LLC , 2018
  4. Abstract:
  5. We present a primal-dual predictor-corrector interior-point method for symmetric cone optimization. The proposed algorithm is based on the Nesterov-Todd search directions and a wide neighborhood, which is an even wider neighborhood than a given negative infinity neighborhood. At each iteration, the method computes two corrector directions in addition to the Ai and Zhang directions (SIAM J. Optim. 16, 400–417, 2005), in order to improve performance. Moreover, we derive the complexity bound of the wide neighborhood predictor-corrector interior-point method for symmetric cone optimization that coincides with the currently best known theoretical complexity bounds for the short step algorithm. Finally, some numerical experiments are provided to reveal the effectiveness of the proposed method. © 2017, Springer Science+Business Media, LLC
  6. Keywords:
  7. Central path ; Euclidean Jordan algebra ; Predictor-corrector interior-point method ; Symmetric cone
  8. Source: Numerical Algorithms ; Volume 78, Issue 2 , 2018 , Pages 535-552 ; 10171398 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s11075-017-0387-9