A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization

This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 32; H. 1; S. 36 - 48
Hauptverfasser:	Che, Hangjun, Wang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Almost-sure convergence Benchmarking Biological neural networks Collaboration Computer Simulation Eigenvalues and eigenfunctions Linear Models Linear programming Mixed integer mixed-integer optimization Neural networks Neural Networks, Computer Neurodynamics Optimization Particle swarm optimization Problem Solving Recurrent neural networks Time
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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