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Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique

Author

Listed:
  • Congying Han
  • Tingting Feng
  • Guoping He
  • Tiande Guo

Abstract

A modified parallel variable distribution (PVD) algorithm for solving large‐scale constrained optimization problems is developed, which modifies quadratic subproblem QPl at each iteration instead of the QPl0 of the SQP‐type PVD algorithm proposed by C. A. Sagastizábal and M. V. Solodov in 2002. The algorithm can circumvent the difficulties associated with the possible inconsistency of QPl0 subproblem of the original SQP method. Moreover, we introduce a nonmonotone technique instead of the penalty function to carry out the line search procedure with more flexibly. Under appropriate conditions, the global convergence of the method is established. In the final part, parallel numerical experiments are implemented on CUDA based on GPU (Graphics Processing unit).

Suggested Citation

  • Congying Han & Tingting Feng & Guoping He & Tiande Guo, 2013. "Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnljam:v:2013:y:2013:i:1:n:295147
    DOI: 10.1155/2013/295147
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    References listed on IDEAS

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    1. Michael C. Ferris, 1994. "Parallel Constraint Distribution in Convex Quadratic Programming," Mathematics of Operations Research, INFORMS, vol. 19(3), pages 645-658, August.
    2. Wenyu Sun & Ya-Xiang Yuan, 2006. "Optimization Theory and Methods," Springer Optimization and Its Applications, Springer, number 978-0-387-24976-6, January.
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    Cited by:

    1. Xiaomei Hu & Zhifeng Xu & Hongxia Cai & Junjun Hu, 2014. "Kinetic‐Monte‐Carlo‐Based Parallel Evolution Simulation Algorithm of Dust Particles," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).

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