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An Inexact Accelerated Proximal Gradient Method and a Dual Newton-CG Method for the Maximal Entropy Problem

Author

Listed:
  • Chengjing Wang

    (Southwest Jiaotong University)

  • Aimin Xu

    (Zhejiang Wanli University)

Abstract

This paper describes an algorithm to solve large-scale maximal entropy problems. The algorithm employs an inexact accelerated proximal gradient method to generate an initial iteration point which is important; then it applies the Newton-CG method to the dual problem. Numerical experiments illustrate that the algorithm can supply an acceptable and even highly accurate solution, while algorithms without generating a good initial point may probably fail.

Suggested Citation

  • Chengjing Wang & Aimin Xu, 2013. "An Inexact Accelerated Proximal Gradient Method and a Dual Newton-CG Method for the Maximal Entropy Problem," Journal of Optimization Theory and Applications, Springer, vol. 157(2), pages 436-450, May.
    • Handle: RePEc:spr:joptap:v:157:y:2013:i:2:d:10.1007_s10957-012-0150-2
    DOI: 10.1007/s10957-012-0150-2
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    Cited by:

    1. Md Sarowar Morshed & Md Saiful Islam & Md. Noor-E-Alam, 2020. "Accelerated sampling Kaczmarz Motzkin algorithm for the linear feasibility problem," Journal of Global Optimization, Springer, vol. 77(2), pages 361-382, June.

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