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A neurodynamic optimization approach for complex-variables programming problem
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DOI：10.1016/j.neunet.2020.06.012.png)
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Abstract
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A neural network model upon differential inclusion is designed for solving the complex-variables convex programming, and the chain rule for real-valued function with the complex-variables is established in this paper. The model does not need to choose penalty parameters when applied to practical problems, which makes it easier to design. The result is obtained that its state reaches the feasible region in finite time. Furthermore, the convergence for its state to an optimal solution is proved. Some typical examples are shown for the effectiveness of the designed model. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Complex variables
Nonsmooth optimization
Convex programming
Neural networks
CR calculus
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