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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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 32; číslo 1; s. 36 - 48
Hlavní autoři:	Che, Hangjun, Wang, Jun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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