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<p>Line search methods typically require a large number of iterations to find the suitable stepsize, resulting in slower convergence speed and higher computation cost. Combining with nonmonotone simulated annealing technique and Armijo line search, we propose a modified three-term conjugate gradient method to reduce the numbers of line search method used. For a given trial stepsize, we decide whether to accept it by simulated annealing rule; if not accepted, Armijo line search is then utilized. Under some mild conditions, the global convergence of the proposed method is established without the gradient Lipschitz continuous condition. Compared to some existing methods for unconstrained optimization problems, numerical experiments demonstrate that the proposed algorithm is promising for the test problems.</p>

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Simulated annealing-based nonmonotone conjugate gradient method for unconstrained optimization with applications

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Yaling Hu,
  • Xu Zhang,
  • Kangkang Deng,
  • Zheng Peng

摘要

Line search methods typically require a large number of iterations to find the suitable stepsize, resulting in slower convergence speed and higher computation cost. Combining with nonmonotone simulated annealing technique and Armijo line search, we propose a modified three-term conjugate gradient method to reduce the numbers of line search method used. For a given trial stepsize, we decide whether to accept it by simulated annealing rule; if not accepted, Armijo line search is then utilized. Under some mild conditions, the global convergence of the proposed method is established without the gradient Lipschitz continuous condition. Compared to some existing methods for unconstrained optimization problems, numerical experiments demonstrate that the proposed algorithm is promising for the test problems.