title: Analysis and design of primal-dual assignment networks.
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 called 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.
authors.affiliation: Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: J
authors.initials: J
authors.lastname: Wang
authors.forename: Y
authors.initials: Y
authors.lastname: Xia