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中文摘要：
共轭梯度法是求解大规模无约束优化问题的一类重要方法．由于共轭梯度法产生的搜索方向不一定是下降方向，为保证每次迭代方向都是下降方向，本文提出一种求解无约束优化问题的谱共轭梯度算法，该方法的每次搜索方向都是下降方向．当假设目标函数一致凸，且其梯度满足Lipschitz条件，线性搜索满足Wolfe条件时，讨论所设计算法的全局收敛性．
英文摘要：
Conjugate gradient methods are class of important methods for unconstrained optimization, especially when the dimension is large. It is well-known that the direction generated by a conjugate gradient method may not be a descent direction. In this paper, we present a spectral conjugate gradient method,which search direction is a descent direction. If the objective function is uniformly convex and its gradient satisfies Lipschitz continuous,the line search satisfies Wolfe condition, the proposed method is shown to be gtohally convergent.
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