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A generalized neural network for distributed nonsmooth optimization with inequality constraint.

Wenwen Jia1, Sitian Qin1, Xiaoping Xue2

  • 1Department of Mathematics, Harbin Institute of Technology, Weihai, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 4, 2019
PubMed
Summary
This summary is machine-generated.

A novel neural network solves distributed optimization problems in multi-agent networks. This method ensures solutions are bounded and reach consensus efficiently, outperforming existing techniques.

Keywords:
ConsensusDistributed nonsmooth constrained optimizationGeneralized neural networkMulti-agent network

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Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Optimization Theory

Background:

  • Distributed optimization is crucial for multi-agent systems.
  • Existing methods often face challenges with non-differentiable problems and complex constraints.
  • Neural networks offer a promising approach for solving these complex optimization tasks.

Purpose of the Study:

  • To propose a generalized neural network for distributed non-differentiable optimization.
  • To ensure bounded solutions and convergence to inequality constraints.
  • To develop a method that is simple and avoids projection operators.

Main Methods:

  • A generalized neural network architecture is introduced.
  • A novel auxiliary function is designed to manage constraints and boundedness.
  • Consensus and convergence properties are analyzed under mild assumptions.

Main Results:

  • The proposed neural network guarantees bounded states and finite-time convergence to constraints.
  • The network achieves consensus and converges to the optimal solution.
  • The method demonstrates a simple structure with fewer state variables than prior approaches.

Conclusions:

  • The presented neural network effectively solves distributed non-differentiable optimization problems.
  • The novel auxiliary function is key to achieving bounded, constrained, and optimal solutions.
  • The approach is practical, as shown by simulations and a power system application.