Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Combinatorial optimization on a Boltzmann machine
Journal of Parallel and Distributed Computing - Neural Computing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Approximating minimum set cover in a Hopfield-style network
Information Sciences: an International Journal
Computational Experience with Approximation Algorithms for the Set Covering Problem
Computational Experience with Approximation Algorithms for the Set Covering Problem
Neural network algorithms for hypergraph optimization problems
Neural network algorithms for hypergraph optimization problems
Graphs and Hypergraphs
Conflict-free star-access in parallel memory systems
Journal of Parallel and Distributed Computing
On the Facial Structure of the Alldifferent System
SIAM Journal on Discrete Mathematics
The constraint of difference and total dual integrality
Proceedings of the 17th Panhellenic Conference on Informatics
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The graph coloring problem is a classic one in combinatorial optimization with a diverse set of significant applications in science and engineering. In this paper, we study several versions of this problem generalized to hypergraphs and develop solutions based on the neural network approach. We experimentally evaluate the proposed algorithms, as well as some conventional ones, on certain types of random hypergraphs. We also evaluate our algorithms on specialized hypergraphs arising in implementations of parallel data structures. The neural network algorithms turn out to be competitive with the conventional ones we study. Finally, we construct a family of hypergraphs that is hard for a greedy strong coloring algorithm, whereas our neural network solutions perform quite well.