一类新的具有充分下降条件和强收敛性的共轭梯度法
A NEW CONJUGATE GRADIENT METHOD WITH STRONGLY GLOBAL CONVERGENCE AND SUFFICIENT DESCENT CONDITION

本文研究了求解无约束优化问题的WYL共轭梯度法.利用修正迭代格式，得到了算法在每步迭代能产生不依赖于搜索条件的充分下降方向.同时，在原算法中关于Wolfe条件中参数去掉的情况下，获得了本文算法是强收敛的.数值实验说明本文算法可以有效求解测试问题.
In this paper,we study the WYL conjugate gradient method for unconstrained optimization problems.By making use of the modified iterative scheme,the sufficient descent conditions are satisfied at each iteration independent of the line search used.Also,by removing the original restriction on the parameter of the Wolfe conditions,we establish the strongly global convergence property for the general function.Numerical results illustrate that our method is efficient for the test problems