Assignment problem

About: Assignment problem is a research topic. Over the lifetime, 7588 publications have been published within this topic receiving 172820 citations. The topic is also known as: marriage problem.

A comparison of two algorithms for determining ranked assignments with application to multitarget tracking and motion correspondence

Abstract: Recently, it has become clear that determining a ranked set of assignments allows computation of very good approximations to the data association problem. Several algorithms have been proposed but only two return the k-best assignments in reasonable time. One is Danchick and Newnams' [1993] algorithm, which is based on the recognition that determining the best assignment is a classical assignment problem and that determining a ranked set of assignments may be accomplished by solving a series of modified copies of the initial assignment problem. The other algorithm is originally due to Murty [1968] and was most recently described within the context of multitarget tracking. We evaluate the two algorithm using randomly generated data and data obtained from an electrooptical sensor simulation in which 90 missiles are launched. These evaluations show that Murty's algorithm perform significantly better in all scenarios. We show the relationship between the two algorithms and how Danchick and Newnam's algorithm can be very easily modified to Murty's algorithm. Experimental results using Murty's algorithm suggest that a solution to the real-time data association problem is now feasible.

The AI conference paper assignment problem

Abstract: The Conference Paper Assignment Problem (CPAP) is the problem of assigning reviewers to conference paper submissions in a manner intended to minimize whingeing It is assumed that papers are reviewed by members of a preset program committee (PC), each of whom has the opportunity to bid on papers prior to the assignment algorithm being run In this survey, we show that CPAP is in P if the only information given is individual program committee members’ preferences for individual papers However, if both preferences and expertise (based on, say, keywords) are given, the problem is potentially more complex

Primal and dual assignment networks

Abstract: This paper presents two recurrent neural networks for solving the assignment problem. Simplifying the architecture of a recurrent neural network based on the primal assignment problem, the first recurrent neural network, called the primal assignment network, has less complex connectivity than its predecessor. The second recurrent neural network, called the dual assignment network, based on the dual assignment problem, is even simpler in architecture than the primal assignment network. The primal and dual assignment networks are guaranteed to make optimal assignment. The applications of the primal and dual assignment networks for sorting and shortest-path routing are discussed. The performance and operating characteristics of the dual assignment network are demonstrated by means of illustrative examples.

Receive-Beam Resource Allocation for Multiple Target Tracking With Distributed MIMO Radars

Abstract: A distributed multiple-input multiple-output (MIMO) radar system is capable of tracking multiple targets under the “defocused transmit-focused receive” (DTFR) operating mode, in which each transmitter forms a completely defocused beam to illuminate the whole surveillance region and each receiver adopts a focused beam to attain a higher resolution. However, when operating in this mode, there exists a resource optimization problem on the allocation of receive-beams. To address this problem, a receive-beam resource allocation (RBRA) strategy is proposed in this paper. The key mechanism is to implement the optimal allocation between receive-beams and targets based on the feedback information in the tracking recursion cycle, with the objective of improving the worst tracking accuracy with multiple targets. Since the posterior Cramer–Rao lower bound (PCRLB) provides a lower bound on the accuracy of target state estimates, it is derived and adopted as an optimization criterion. It is shown that the optimal RBRA is a multidimensional nonconvex assignment problem that is NP-hard. We propose an efficient convex relaxation optimization to solve it. Numerical results demonstrate the superior performance of the proposed strategy in terms of the worst case tracking root mean-square errors.