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A New Spectral Three-Term Conjugate Gradient Method with Random Parameter Based on Modified Secant Equation and Its Application to Low-Carbon Supply Chain Optimization

Author

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  • Guoling Zhou
  • Yueting Yang
  • Mingyuan Cao
  • Shaojian Qu

Abstract

In this work, considering the advantages of spectral conjugate gradient method and quasi-Newton method, a spectral three-term conjugate gradient method with random parameter is proposed. The parameter in the search direction of the new method is determined by minimizing the Frobenius norm of difference between search direction matrix and self-scaled memoryless BFGS matrix based on modified secant equation. Then, the search direction satisfying the sufficient descent condition is obtained. The global convergence of new method is proved under appropriate assumptions. Numerical experiments show that our method has better performance by comparing with the up-to-date method. Furthermore, the new method has been successfully applied to the optimization of low-carbon supply chain.

Suggested Citation

  • Guoling Zhou & Yueting Yang & Mingyuan Cao & Shaojian Qu, 2022. "A New Spectral Three-Term Conjugate Gradient Method with Random Parameter Based on Modified Secant Equation and Its Application to Low-Carbon Supply Chain Optimization," Journal of Mathematics, Hindawi, vol. 2022, pages 1-15, October.
    • Handle: RePEc:hin:jjmath:8939770
    DOI: 10.1155/2022/8939770
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    Cited by:

    1. Roberto León & Pablo A. Miranda-Gonzalez & Francisco J. Tapia-Ubeda & Elias Olivares-Benitez, 2024. "An Inventory Service-Level Optimization Problem for a Multi-Warehouse Supply Chain Network with Stochastic Demands," Mathematics, MDPI, vol. 12(16), pages 1-20, August.

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