（补）最优化方法 Optimization Method

随机梯度下降

mini batch

批梯度下降算法

牛顿法

考虑这样一个问题，找到函数值为0的点，对于方程，目标是找到一个，使得，这里是一个实数，牛顿法是按照如下方式进行的：

一维情况下，牛顿方法可以简单理解为：随机选取一个点，然后求出f在该点处的切线L，该切线L的斜率即f在该点处倒数，该切线与x轴相交的下一个点即作为下一次迭代的值，这样知道收敛便可求得，过程如下图所示：

在优化方法中，极值点事一阶导函数为0的点，即f’(x)=0,对于一个方程，如何求得其极值点呢，只需对上述公式做如下修改：

推广到向量形式便得到了 Newton-Raphson method ，形式如下：

这里H叫做Hessian矩阵，形式如下：

L-BFGS

时间： 2024-08-13 21:07:18

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