Laplacian

参考文献

https://en.wikipedia.org/wiki/Second_derivative

拉普拉斯是将二阶导数推广到高维空间的一种方式。
Another common generalization of the second derivative is the Laplacian. This is the differential operator $\nabla^{2}$∇2 defined by
$\nabla^{2} f=\frac{\partial^{2} f}{\partial x^{2}}+\frac{\partial^{2} f}{\partial y^{2}}+\frac{\partial^{2} f}{\partial z^{2}}$∇2f=∂x2∂2f​+∂y2∂2f​+∂z2∂2f​

The Laplacian of a function is equal to the divergence of the gradient and the trace of the Hessian matrix.

什么是Hessian matrix？什么是梯度的divergence?
先不研究。