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<p>In this paper, a family of modified DY–type conjugate gradient methods is proposed for unconstrained optimization. Unlike the classical DY method, whose search directions usually satisfy the descent property under the standard Wolfe line search, the proposed methods always guarantee the descent property independently of any line search. Furthermore, under the standard Wolfe line search and the usual assumptions on the objective function, the global convergence of the proposed methods is established. To assess the numerical performance, we further design a specific conjugate gradient method from the proposed family and perform numerical experiments on large-scale unconstrained optimization and image denoising problems. The numerical results show that the resulting method is effective and competitive compared with several existing methods.</p>

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A family of modified DY–type conjugate gradient methods with applications to image denoising

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Yu Cai,
  • Guanghui Zhou,
  • Chenyun Mo

摘要

In this paper, a family of modified DY–type conjugate gradient methods is proposed for unconstrained optimization. Unlike the classical DY method, whose search directions usually satisfy the descent property under the standard Wolfe line search, the proposed methods always guarantee the descent property independently of any line search. Furthermore, under the standard Wolfe line search and the usual assumptions on the objective function, the global convergence of the proposed methods is established. To assess the numerical performance, we further design a specific conjugate gradient method from the proposed family and perform numerical experiments on large-scale unconstrained optimization and image denoising problems. The numerical results show that the resulting method is effective and competitive compared with several existing methods.