The linear assignment problem is a fundamental problem in combinatorial optimization with a wide range of applications, from operational research to data science. It consists of assigning “agents” to “tasks” on a one-to-one basis, while minimizing the total cost associated with the assignment. While many exact algorithms have been developed to identify such an optimal assignment, most of these methods are computationally prohibitive for large size problems. In this paper, we propose an alternative approach to solving the assignment problem using techniques adapted from statistical physics. Our first contribution is to fully describe this formalism, including all the proofs of its main claims. In particular we derive a strongly concave effective free-energy function that captures the constraints of the assignment problem at a finite temperature. We prove that this free energy decreases monotonically as a function of $\beta$, the inverse of temperature, to the optimal assignment cost, providing a robust framework for temperature annealing. We prove also that for large enough $\beta$ values the exact solution to the generic assignment problem can be derived using simple roundoff to the nearest integer of the elements of the computed assignment matrix. Our second contribution is to derive a provably convergent method to handle degenerate assignment problems, with a characterization of those problems. We describe computer implementations of our framework that are optimized for parallel architectures, one based on CPU, the other based on GPU. We show that the latter enables solving large assignment problems (of the orders of a few 10 000s) in computing clock times of the orders of minutes.

• Received 2 December 2020
• Accepted 1 March 2021

DOI:https://doi.org/10.1103/PhysRevE.103.042101