## Journal of Applied Mathematics

### Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique

#### Abstract

A modified parallel variable distribution (PVD) algorithm for solving large-scale constrained optimization problems is developed, which modifies quadratic subproblem $Q{P}_{l}$ at each iteration instead of the $Q{P}_{l}^{\mathrm{0}}$ 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 $Q{P}_{l}^{\mathrm{0}}$ 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).

#### Article information

Source
J. Appl. Math., Volume 2013 (2013), Article ID 295147, 7 pages.

Dates
First available in Project Euclid: 14 March 2014

https://projecteuclid.org/euclid.jam/1394807928

Digital Object Identifier
doi:10.1155/2013/295147

Mathematical Reviews number (MathSciNet)
MR3039741

Zentralblatt MATH identifier
1266.65099

#### Citation

Han, Congying; Feng, Tingting; He, Guoping; Guo, Tiande. Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique. J. Appl. Math. 2013 (2013), Article ID 295147, 7 pages. doi:10.1155/2013/295147. https://projecteuclid.org/euclid.jam/1394807928

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