Time-slot assignment for TDMA-systems
Computing
Computational Optimization and Applications
On the expected optimal value of random assignment problems: experimental results and open questions
Computational Optimization and Applications
An efficient cost scaling algorithm for the assignment problem
Mathematical Programming: Series A and B
Algorithms and codes for dense assignment problems: the state of the art
Discrete Applied Mathematics
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selected topics on assignment problems
Discrete Applied Mathematics
Monte carlo em for data-association and its applications in computer vision
Monte carlo em for data-association and its applications in computer vision
Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
Asymptotic behavior of the expected optimal value of the multidimensional assignment problem
Mathematical Programming: Series A and B
An algorithm for ranking assignments using reoptimization
Computers and Operations Research
Hi-index | 0.00 |
In this paper, we consider three alternative primal models and their corresponding alternative dual models for the linear assignment problem. We then define the concept of Negative Dual Rectangle (NDR) and suggest an algorithm that solves two of these dual problems by repeatedly finding and cancelling NDRs until it yields an optimal solution to the assignment problem. The algorithm is simple, flexible, efficient, and unified. We also introduce the notion of partial zero cover as an interpretation of an NDR. We then introduce some heuristic methods for finding NDRs. We also state and prove a lemma to establish the optimal use of an NDR. Furthermore, we show that on a new class of benchmark instances that is introduced in this paper the running time of our algorithm is highly superior to a well-known pure shortest path algorithm.