共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。-Conjugate Gradient Method (Conjugate Gradient) is between the steepest descent method between a law and Newton' s method, it is only the first order derivative information, but the steepest descent method overcomes the shortcomings of slow convergence and avoid the need to store the Newton law Hesse and cons of computing the inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, but also large-scale nonlinear optimization solution of one of the most effective algorithm.