Analysis and design of primal-dual assignment networks

Jun Wang, Youshen Xia

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

The assignment problem is an archetypical combinatorial optimization problem having widespread applications. This paper presents two recurrent neural networks, a continuous-time one and a discrete-time one, for solving the assignment problem. Because the proposed recurrent neural networks solve the primal and dual assignment problems simultaneously, they are named as the primal-dual assignment networks. The primal-dual assignment networks are guaranteed to make optimal assignment regardless of initial conditions. Unlike the primal or dual assignment network, there is no time-varying design parameter in the primal-dual assignment networks. Therefore, they are more suitable for hardware implementation. The performance and operating characteristics of the primal-dual assignment networks are demonstrated by means of illustrative examples.

Original languageEnglish
Pages (from-to)183-194
Number of pages12
JournalIEEE Transactions on Neural Networks
Volume9
Issue number1
DOIs
Publication statusPublished - 1998
Externally publishedYes

Keywords

  • Assignment problem
  • Discrete-time systems
  • Optimization techniques
  • Recurrent neural networks
  • Shortest path problem
  • Sorting problem

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