This paper gives a modified Hestenes and Stiefel (HS) conjugate gradient algorithm under the Yuan-Wei-Lu inexact line search technique for large-scale unconstrained optimization problems, where the proposed algorithm has the following properties the new search direction possesses not only a sufficient descent property but also a trust region feature; the presented algorithm has global convergence for nonconvex functions; the numerical experiment showed that the new algorithm is more effective than similar algorithms.
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机译：针对大规模无约束优化问题，提出了一种元-鲁-鲁不精确线搜索技术下的改进的Hestenes and Stiefel（HS）共轭梯度算法，该算法具有以下特点，新的搜索方向不仅具有足够的血统属性，但又是信任区特征;该算法对非凸函数具有全局收敛性。数值实验表明，新算法比同类算法更有效。
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