共轭梯度法

  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。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcoming of the steepest descent method and avoids the need for the Newton method to store. And the disadvantage of calculating the Hesse matrix and inverting, the conjugate gradient method is not only one of the most useful methods for solving large linear equations, but also one of the most effective algorithms for solving large-scale nonlinear optimization. Among various optimization algorithms, the conjugate gradient method is a very important one. The advantage is that the required amount of storage is small, step convergence, high stability, and no external parameters are required.)

共轭梯度法, 0 , 2018-11-26
共轭梯度法\frcg.m, 914 , 2009-09-21
共轭梯度法\fun.m, 52 , 2009-08-29
共轭梯度法\gfun.m, 81 , 2009-08-29