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In recent years, matrix-valued optimization algorithms have been studied to enhance the computational performance of vector-valued optimization algorithms. This paper presents two matrix-type projection neural networks, continuous-time and discrete-time models, for solving matrix-valued optimization problems. The proposed continuous-time neural network may be viewed as a significant extension to the vector-type double projection neural network. More importantly, the proposed discrete-time projection neural network can be parallelly implemented in terms of matrix state space. Under pseudo-monotonicity condition and Lipschitz continuous condition, it is guaranteed that the two proposed matrix-type projection neural networks are globally convergent to the optimal solution. Finally, computed examples show that the two proposed matrix-type projection neural networks are much superior to the vector-type projection neural network in computation speed. © 2018, Springer Nature Switzerland AG.
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ISSN: 0302-9743
Year: 2018
Volume: 11302 LNCS
Page: 405-416
Language: English
0 . 4 0 2
JCR@2005
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WoS CC Cited Count: 0
SCOPUS Cited Count: 1
ESI Highly Cited Papers on the List: 0 Unfold All
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30 Days PV: 1
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