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Jacobi matrix雅克比矩陣

cite from wikipedia

Vector calculus

Vector-by-vector

Each of the previous two cases can be considered as an application of the derivative of a vector with respect to a vector, using a vector of size one appropriately. Similarly we will find that the derivatives involving matrices will reduce to derivatives involving vectors in a corresponding way.

The derivative of a vector function (a vector whose components are functions) \mathbf{y} =\begin{bmatrix}y_1 \\y_2 \\\vdots \\y_m \\\end{bmatrix}, of an independent vector \mathbf{x} =\begin{bmatrix}x_1 \\x_2 \\\vdots \\x_n \\\end{bmatrix}, is written (in numerator layout notation) as

\frac{\partial \mathbf{y}}{\partial \mathbf{x}} =\begin{bmatrix}\frac{\partial y_1}{\partial x_1} & \frac{\partial y_1}{\partial x_2} & \cdots & \frac{\partial y_1}{\partial x_n}\\\frac{\partial y_2}{\partial x_1} & \frac{\partial y_2}{\partial x_2} & \cdots & \frac{\partial y_2}{\partial x_n}\\\vdots & \vdots & \ddots & \vdots\\\frac{\partial y_m}{\partial x_1} & \frac{\partial y_m}{\partial x_2} & \cdots & \frac{\partial y_m}{\partial x_n}\\\end{bmatrix}.

In vector calculus, the derivative of a vector function y with respect to a vector x whose components represent a space is known as the pushforward or differential

, or the .

The pushforward along a vector function f with respect to vector v in Rm is given by d\,\mathbf{f}(\mathbf{v}) = \frac{\partial \mathbf{f}}{\partial \mathbf{x}} \mathbf{v}.

在向量微積分中,向量函式y關於x(x的元素代表空間)的導數被認為是雅克比矩陣