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<p>This paper introduces an Adaptive Nonmonotone Nonsmooth (ANN) optimization algorithm designed to address optimization challenges of varying scales. The proposed method extends the Zhang-Hager non-monotonic line search technique to nonsmooth functions, and establishes the algorithm’s global convergence theoretically. Key contributions include the introduction of non-monotonic descent directions and Armijo conditions, adaptive parameter control, and rigorous theoretical analysis. Numerical experiments demonstrate the algorithm’s effectiveness in solving nonsmooth optimization problems, particularly in high-dimensional settings.</p>

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An adaptive nonmonotone line search technique for nonsmooth optimization

  • 期刊论文

  • 发表日期：

    2025年11月20日

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  • Mei Wang,
  • Xiaojun Zhang

摘要

This paper introduces an Adaptive Nonmonotone Nonsmooth (ANN) optimization algorithm designed to address optimization challenges of varying scales. The proposed method extends the Zhang-Hager non-monotonic line search technique to nonsmooth functions, and establishes the algorithm’s global convergence theoretically. Key contributions include the introduction of non-monotonic descent directions and Armijo conditions, adaptive parameter control, and rigorous theoretical analysis. Numerical experiments demonstrate the algorithm’s effectiveness in solving nonsmooth optimization problems, particularly in high-dimensional settings.