Name
Abstract | ||
|---|---|---|
Semidefinite programming problem is an important optimization problem that has been extensively investigated. A real-time solution method for solving such a problem, however, is still not yet available. This paper proposes a novel recurrent neural network for this purpose. First, an auxiliary cost function is introduced to minimize the duality gap between the admissible points of the primal problem and the corresponding dual problem. Then a dynamical system is constructed to drive the duality gap to zero exponentially along any trajectory by modifying the gradient of the auxiliary cost function. Furthermore, a subsystem is developed to circumvent in the computation of matrix inverse, so that the resulting overall dynamical system can be realized using a recurrent neural network. The architecture of the resulting neural network is discussed. The operating characteristics and performance of the proposed approach are demonstrated by means of simulation results. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/72.737496 | IEEE Transactions on Neural Networks |
Keywords | DocType | Volume |
neural network,overall dynamical system,duality gap,dynamical system,primal problem,mathematical programming,important optimization problem,matrix algebra,semidefinite programming problem,corresponding dual problem,matrix inverse,real-time semidefinite programming,auxiliary cost function,dual problem,recurrent neural nets,recurrent neural network,duality (mathematics),manufacturing,cost function,indexing terms,optimization problem,linear programming,dynamic system,linear matrix inequality,duality mathematics,computer networks,real time,recurrent neural networks,vectors,neural networks,quadratic programming,constraint optimization | Journal | 10 |
Issue | ISSN | Citations |
1 | 1045-9227 | 5 |
PageRank | References | Authors |
0.46 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danchi Jiang | 1 | 216 | 15.57 |
| Jun Wang | 2 | 9228 | 736.82 |