On the Asymptotic Properties of Random Multidimensional Assignment Problems

The Multidimensional Assignment Problem (MAP) is a NP-hard combinatorial optimization problem, occurring in many applications, such as data association. In this paper, we prove two conjectures made in [1] and based on data from computational experiments on MAPs. We show that the mean optimal objective function cost of random instances of the MAP goes to zero as the problem size increases, when assignment costs are independent exponentially or uniformly distributed random variables. We also prove that the mean optimal solution goes to negative infinity when assignment costs are independent normally distributed random variables.