中文摘要：

基于信赖域技术和修正拟牛顿方程，结合Neng—Zhu Gu非单调策略，设计新的求解无约束最优化问题的非单调超记忆梯度算法，分析算法的收敛性和收敛速度。新算法每次迭代节约了矩阵的存储量和计算量，算法稳定，适于求解大规模问题。数值试验结果表明新算法是有效的。

英文摘要：

Based on trust region technique and modified quasi-Newton equation, by combining with Neng-Zhu Gu non-monotone strategy, a new super-memory gradient method for unconstrained optimization problem was presented. The global and convergence properties of the new method were proved. It saves the storage and computation of some matrixes in its iteration, and is suitable for solving large scale optimization problems. The numerical results show that the new method is effective.