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Predefined-time with time-varying coefficients neurodynamic for composite optimization problems

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Listed:
  • Yu, Dongmei
  • Lin, Shaowei
  • Zhang, Gehao
  • Yin, Hongrui

Abstract

In this paper, we propose a predefined-time with time-varying coefficients neurodynamic (PTTVCN) model to solve composite optimization problems (COPs). We first present the Lyapunov stability conditions for predefined-time stability in time-varying dynamical system and provide specific inferences under different time-varying coefficients. We then propose the PTTVCN model to solve COPs based on the predefined-time stability conditions of time-varying dynamical system. Theoretical analysis verifies that the PTTVCN model can achieve uniform convergence within predefined time and possesses a certain degree of noise resistance. Simulation results are given to show the effectiveness of the proposed predefined-time stability neurodynamic model with time-varying coefficients for COPs. Finally, numerical experiments on both image restoration and Poisson regression problems validate the superiority of the proposed method.

Suggested Citation

  • Yu, Dongmei & Lin, Shaowei & Zhang, Gehao & Yin, Hongrui, 2025. "Predefined-time with time-varying coefficients neurodynamic for composite optimization problems," Chaos, Solitons & Fractals, Elsevier, vol. 199(P3).
    • Handle: RePEc:eee:chsofr:v:199:y:2025:i:p3:s0960077925008057
    DOI: 10.1016/j.chaos.2025.116792
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    References listed on IDEAS

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    1. Vassilis Apidopoulos & Nicolò Ginatta & Silvia Villa, 2022. "Convergence rates for the heavy-ball continuous dynamics for non-convex optimization, under Polyak–Łojasiewicz condition," Journal of Global Optimization, Springer, vol. 84(3), pages 563-589, November.
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