Acta mathematica scientia,Series A ›› 2012, Vol. 32 ›› Issue (2): 336-343.

• Articles • Previous Articles     Next Articles

A Modified SQP Parallel Variable Distribution Algorithm

 FENG Ting-Ting1, HAN Cong-Ying1, 2**, HE Guo-Ping1   

  1. 1.College of Information Science and Technology, Shandong University of Science and Technology, Shandong |Qingdao 266510;
    2.School of Mathematical Sciences, Graduate University of Chinese Academy of Sciences, Beijing 100049
  • Received:2010-11-21 Revised:2011-12-30 Online:2012-04-25 Published:2012-04-25
  • Contact: HAN Cong-Ying,congyh@sdust.edu.cn E-mail:fuchen_ftt@163.com; congyh@sdust.edu.cn; hegp@263.net
  • Supported by:

    国家自然科学基金(11101420, 10971122)、高等学校博士点基金(20093718110005)、山东省科技攻关(2009GG10001012)和山东省自然科学基金(Y2008A01)资助

Abstract:

Ferris and Mangasarian proposed a PVD(parallel variable distribution) algorithm for solving optimization problems, which divides variables into primary and secondary variables groups. According to the algorithm, the variables are distributed among p parallel processors with each processor having the responsibility for updating its primary variables while allowing the remaining "secondary" variables to change in a restricted fashion along some easily computable directions, which enhances robustness and flexibility of the algorithm. In this paper, we present a modified SQP type PVD algorithm based on [6], whose search direction is a suitable combination of a descent direction and a feasible direction, and give a second-order revised for such a  direction. This new algorithm is very effective in preventing Maratos effect from happening, and avoid constraints in subproblem are inconsistent. We show the global convergence under some suitable conditions.

Key words: Nonlinear programming, Sequential quadratic programming, PVD algorithm

CLC Number: 

  • 46N10
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