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Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique

Congying Han, Tingting Feng, Guoping He, Tiande Guo
School of Mathematical Sciences, University of the Chinese Academy of Sciences, No. 19(A), Yuquan Road, Shijingshan District, Beijing 100049, China
Journal of Applied Mathematics, Article ID 295147, Volume 2013, 2013

@article{guo2013parallel,

   title={Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique},

   author={Guo, Tiande},

   journal={Journal of Applied Mathematics},

   volume={2013},

   year={2013},

   publisher={Hindawi Publishing Corporation}

}

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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 QPl of the SQP-type PVD algorithm proposed by C. A. Sagastizabal and M. V. Solodov in 2002. The algorithm can circumvent the difficulties associated with the possible inconsistency of 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).
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