Name
Title | ||
|---|---|---|
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 QP(l) at each iteration instead of the QP(l)(0) 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 QP(l)(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). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1155/2013/295147 | JOURNAL OF APPLIED MATHEMATICS |
Field | DocType | Volume |
Convergence (routing),Mathematical optimization,Estimation of distribution algorithm,CUDA,Line search,Sequential quadratic programming,Graphics processing unit,Mathematics,Constrained optimization,Penalty method | Journal | 2013 |
Issue | ISSN | Citations |
null | 1110-757X | 4 |
PageRank | References | Authors |
0.42 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congying Han | 1 | 17 | 4.07 |
| Tingting Feng | 2 | 11 | 1.33 |
| Guoping He | 3 | 91 | 13.59 |
| Tiande Guo | 4 | 67 | 7.35 |