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中文摘要：
1引言 考虑无约束优化问题（P）：inin x∈ R^n f（x）,其中f（x）：R^n→＋R^1是一阶连续可微函数．求解问题（P）的拟牛顿算法收敛速度快，每次迭代不需要计算目标函数的Hesse矩阵及其逆矩阵，
英文摘要：
We propose a modified non-monotone step size rule and analyze the global convergence of a new diagonal-sparse quasi-Newton method. The new step size rule is similar to the Grippo non-monotone step size rule and contains it as a special case. We can choose a larger stepsize in each line search procedure and main- tain the global convergence property of our diagonal-sparse quasi-Newton method under the assumption that Vf（x） is uniformly continuous, and further analyze the superlinear convergence property of the new method. Numerical results show that the new algorithm is efficient and suitable to solve large scale problems.
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