Understanding Matrix Calculus: Laplacian, Hessian, and Jacobian Explained

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Matrix calculus involves key concepts such as the Jacobian, Hessian, and Laplacian. The Jacobian is represented as a matrix of first derivatives, while the Hessian is a matrix of second derivatives. The Laplacian, defined as the divergence of the gradient, can be expressed as the second derivative but is considered a scalar quantity. There is a noted confusion regarding the definitions of the Laplacian and Hessian, particularly in their dimensional representations. Ultimately, the Laplacian is understood as the trace of the Hessian, clarifying their relationship.
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Hellow!

I was studying matrix calculus and learned new things as:
\frac{d\vec{y}}{d\vec{x}}=\begin{bmatrix} \frac{dy_1}{dx_1} & \frac{dy_1}{dx_2} \\ \frac{dy_2}{dx_1} & \frac{dy_2}{dx_2} \\ \end{bmatrix}
\frac{d}{d\vec{r}}\frac{d}{d\vec{r}} = \frac{d^2}{d\vec{r}^2} = \begin{bmatrix} \frac{d^2}{dxdx} & \frac{d^2}{dydx}\\ \frac{d^2}{dxdy} & \frac{d^2}{dydy}\\ \end{bmatrix}
Those are the real definition for Jacobian and Hessian. However, the definition for Laplacian is ##\triangledown \cdot \triangledown = \triangledown^2##, that corresponds to ##\frac{d}{d\vec{r}} \cdot \frac{d}{d\vec{r}} = \frac{d^2}{d\vec{r}^2}##, but this definition conflicts with the definition for Hessian that is ##\frac{d^2}{d\vec{r}^2}## too. So, where is the mistake with respect to these definitions? I learned something wrong?
 
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Hello Jhenrique! :smile:

The Hessian is a 2x2 matrix, (column-vector)(row-vector).

The Laplacian is a 1x1 matrix, (row-vector)(column-vector). :wink:
 
The laplacian is the trace of the hessian.
 
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