CAJ | 학술논문

针对参数βk的不同选取可以构成不同的共轭梯度法，给出了一类求解无约束最优化问题的修正的共轭梯度算法，这种算法能够在较弱条件下证明选定的卢。在每一步都能产生一个下降方向，且在Wolfe线搜索下具有全局收敛性．另外这种算法在另一种Wolfe搜索条件下，若搜索方向为下降时，也具有全局收敛性．
침대삼수βk적불동선취가이구성불동적공액제도법，급출료일류구해무약속최우화문제적수정적공액제도산법，저충산법능구재교약조건하증명선정적로。재매일보도능산생일개하강방향，차재Wolfe선수색하구유전국수렴성．령외저충산법재령일충Wolfe수색조건하，약수색방향위하강시，야구유전국수렴성．
The conjugate gradient method is an effective method to solve the large-scale optimization problems, and it is widely used in practice. On the basis of the choice of the parameter βk, there are many different conjugate gradient methods. This paper proposed a class of new conjugate gradient methods for solving the unconstrained optimization problem. And these methods can prove that the parameters βk selected and produce a descent search direction at every iteration and is globally convergent under the Wolfe line search conditions.