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A partitioned PSB method for partially separable unconstrained optimization problems

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  • Cao, Huiping
  • Yao, Lan

Abstract

In this paper, we propose a partitioned PSB method for solving partially separable unconstrained optimization problems. By using a projection technique, we construct a sufficient descent direction. Under appropriate conditions, we show that the partitioned PSB method with projected direction is globally and superlinearly convergent for uniformly convex problems. In particular, the unit step length is accepted after finitely many iterations. Finally, some numerical results are presented, which show that the partitioned PSB method is effective and competitive.

Suggested Citation

  • Cao, Huiping & Yao, Lan, 2016. "A partitioned PSB method for partially separable unconstrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 290(C), pages 164-177.
    • Handle: RePEc:eee:apmaco:v:290:y:2016:i:c:p:164-177
    DOI: 10.1016/j.amc.2016.06.009
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    References listed on IDEAS

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    1. Xiao-Min An & Dong-Hui Li & Yunhai Xiao, 2011. "Sufficient descent directions in unconstrained optimization," Computational Optimization and Applications, Springer, vol. 48(3), pages 515-532, April.
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