Asymptotic experimental analysis for the Held-Karp traveling salesman bound
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
Neural networks for process scheduling in real-time communication systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
“Optimal” Hopfield network for combinatorial optimization with linear cost function
IEEE Transactions on Neural Networks
Extended Hopfield models for combinatorial optimization
IEEE Transactions on Neural Networks
Hopfield Network as Static Optimizer: Learning the Weights and Eliminating the Guesswork
Neural Processing Letters
Motion planning in order to optimize the length and clearance applying a Hopfield neural network
Expert Systems with Applications: An International Journal
A Hopfield neural network applied to the fuzzy maximum cut problem under credibility measure
Information Sciences: an International Journal
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A simulation methodology, which trades space complexity with time complexity, to create the Hopfield neural network weight matrix, the costliest data structure for simulation of Hopfield neural network algorithm for large-scale optimization problems, is proposed. Modular composition of a weight term of the Hopfield neural network weight matrix for a generic static optimization problem, which facilitates construction and reconstruction of the weights on demand during a simulation, is exposed. Proposed methodology is demonstrated on a static combinatorial optimization problem, namely the Traveling Salesman Problem (TSP), through the algebraic procedure for temporal (versus spatial) weight matrix construction, pseudo code and C/C++ code implementation, and an associated simulation study. The proposed methodology is successfully tested through simulation on a general purpose WindowsTM-AMDTM platform for up to 1000 city Traveling Salesman Problem instance, which would require approximately no less than 1TB of memory to be allocated simply to instantiate the weight matrix in the memory space of the simulation process.