Can the Second Derivative of a Function be Compact?

In summary, given a function f(x(t, s) y(t, s)), if is possible to compact \frac{∂f}{∂t}=\frac{∂f}{∂x} \frac{∂x}{∂t}+\frac{∂f}{∂y} \frac{∂y}{∂t}by using the Hessian matrix. Analogously, isn't possible to compact the sencond derivate.
  • #1
Jhenrique
685
4
Given a function f(x(t, s) y(t, s)), if is possible to compact
[tex]\frac{∂f}{∂t}=\frac{∂f}{∂x} \frac{∂x}{∂t}+\frac{∂f}{∂y} \frac{∂y}{∂t}[/tex]
by
[tex]\frac{df}{dt}=\bigtriangledown f\cdot \frac{d\vec{r}}{dt}[/tex]

So, analogously, isn't possible to compact the sencond derivate
[tex]\frac{\partial^2 f}{\partial s \partial t} = \frac{\partial^2 f}{\partial x^2} \frac{\partial x}{\partial s }\frac{\partial x}{\partial t } + \frac{\partial^2 f}{\partial x \partial y}\left( \frac{\partial y}{\partial s }\frac{\partial x}{\partial t } + \frac{\partial x}{\partial s }\frac{\partial y}{\partial t }\right) + \frac{\partial^2 f}{\partial y^2} \frac{\partial y}{\partial s }\frac{\partial y}{\partial t }+\frac{\partial f}{\partial x}\frac{\partial^2 x}{\partial s \partial t}+\frac{\partial f}{\partial y}\frac{\partial^2 y}{\partial s \partial t}[/tex]
using matrix with the matrix Hessian(f) ?
 
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  • #2
[tex]\frac{\partial}{\partial t} = \sum_{i} \frac{\partial x_i}{\partial t}\frac{\partial}{\partial x_i} [/tex]
[tex]\frac{\partial}{\partial s} = \sum_{i} \frac{\partial x_i}{\partial s}\frac{\partial}{\partial x_i} [/tex]
[tex]\frac{\partial^2}{\partial s\partial t}f = \sum_{i,j} \frac{\partial x_i}{\partial s}\frac{\partial}{\partial x_i} \frac{\partial x_j}{\partial t}\frac{\partial f}{\partial x_j} [/tex]
Next I applied the product rule:
[tex]\frac{\partial^2}{\partial s\partial t}f = \sum_{i,j} \frac{\partial x_i}{\partial s}\left( \frac{\partial^2 x_j}{\partial x_i \partial t}\frac{\partial f}{\partial x_j} +\frac{\partial x_j}{\partial t}\frac{\partial f}{\partial x_i \partial x_j} \right) [/tex]
The second term above involves the Hessian matrix, which is sandwiched between [itex]\partial \mathbf{x}^T/\partial s[/itex] and [itex]\partial \mathbf{x}/\partial t[/itex]. I've moved this to become the first term.
[tex]\frac{\partial^2}{\partial s\partial t}f = \sum_{i,j} \frac{\partial x_i}{\partial s} H_{ij}(f)\frac{\partial x_j}{\partial t} + \sum_{i,j}\frac{\partial x_i}{\partial s}\frac{\partial^2 x_j}{\partial x_i \partial t}\frac{\partial f}{\partial x_j} [/tex]
Finally I reverse chain ruled the second term.
[tex]\frac{\partial^2}{\partial s\partial t}f = \sum_{i,j} \frac{\partial x_i}{\partial s} H_{ij}(f)\frac{\partial x_j}{\partial t} + \sum_{j}\frac{\partial^2 x_j}{\partial s \partial t}\frac{\partial f}{\partial x_j} [/tex]

You may want to check my math.
 
  • #3
Your math appears to be correct. But, I don't know how you and outhers are capable to comprehend this summation notation!

I'm looking for matrix, without summation.
 
  • #4
How's this?

[tex]\frac{\partial^2 f}{\partial s \partial t} = \frac{\partial \mathbf{x}^T}{\partial s} \mathbf{H}(f)\frac{\partial \mathbf{x}}{\partial t} + \frac{\partial^2 \mathbf{x}}{\partial s \partial t} \cdot \nabla f[/tex]
 
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  • #5
Oh yeah! This is what I was looking for! I never would be capable to deduce this!
By the way, it's complicated!
thx!
 
  • #6
Moreover... in matricial form, could be wrote so:
[tex]\begin{bmatrix} \frac{\partial^2 f}{\partial x\partial x} & \frac{\partial^2 f}{\partial x\partial y}\\ \frac{\partial^2 f}{\partial y\partial x} & \frac{\partial^2 f}{\partial y\partial y} \end{bmatrix} \cdot \begin{bmatrix} \frac{\partial x}{\partial s} \frac{\partial x}{\partial t} & \frac{\partial x}{\partial s} \frac{\partial y}{\partial t}\\ \frac{\partial y}{\partial s} \frac{\partial x}{\partial t} & \frac{\partial y}{\partial s} \frac{\partial y}{\partial t} \end{bmatrix} + \begin{bmatrix} \frac{\partial f}{\partial x} \\ \frac{\partial f}{\partial y} \end{bmatrix} \cdot \begin{bmatrix} \frac{\partial^2 x}{\partial s\partial t}\\ \frac{\partial^2 y}{\partial s\partial t} \end{bmatrix}[/tex]

But, I ask... exist dot product between matrices (tensors of rank 2)?

And is possible call the second matrix of something more simplificated? The first is H(f), the third is ∇f and the fourth is ∂²x/∂s∂t...
 

1. What is the second derivative of a tensor?

The second derivative of a tensor is a mathematical concept that describes the rate of change of the first derivative of a tensor. It is a way to quantify how much a tensor is changing, and in what direction, at a specific point in space.

2. How is the second derivative of a tensor calculated?

The second derivative of a tensor can be calculated using the tensor calculus operation known as the covariant derivative. This involves taking the first derivative of the tensor with respect to each coordinate, and then taking the derivative of those derivatives.

3. What is the significance of the second derivative in physics?

In physics, the second derivative of a tensor is important because it helps to describe the curvature of space and time. It is used in theories such as general relativity to describe how matter and energy affect the curvature of spacetime.

4. Can the second derivative of a tensor be negative?

Yes, the second derivative of a tensor can be negative. This would indicate that the rate of change of the first derivative is decreasing in a specific direction. It is possible for the second derivative to be negative in one direction and positive in another, indicating a change in curvature.

5. How is the second derivative of a tensor used in machine learning?

In machine learning, the second derivative of a tensor is used to optimize algorithms and improve their performance. This involves calculating the Hessian matrix, which is a matrix of second derivatives, to determine the curvature of the cost function and guide the learning process.

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