Integrating derivatives in matrix elements

In summary, the conversation discusses two problems involving finding a function f that satisfies a given equation involving a fixed function g and a constant vector u. The first problem is relatively easy and a solution formula is provided. However, in the second problem where g and u have more complex forms, the previous solution formula does not work. The conversation then explores the special case where g=0 and a formal solution is given, but the matrices involved cannot be diagonalized simultaneously. The conversation ends with a clarification about the assumptions made for the solution formula to work.
  • #1
jostpuur
2,116
19
First a little warm up problem. Suppose [itex]g:\mathbb{R}^N\to\mathbb{C}[/itex] is some fixed function, and we want to find [itex]f:\mathbb{R}^N\to\mathbb{C}[/itex] such that

[tex]
g(x) = u\cdot\nabla_x f(x)
[/tex]

holds, where [itex]u\in\mathbb{R}^N[/itex] is some constant. The problem is not extremely difficult, and after some work the following formula can be found:

[tex]
f(x) = f\Big(x - \frac{u\cdot x}{\|u\|^2}u\Big) + \int\limits_0^{(x\cdot u)/\|u\|^2} g\Big(x - \frac{u\cdot x}{\|u\|^2}u+ \nu u\Big) d\nu
[/tex]

If it is assumed that [itex]f,g[/itex] are differentiable, the formula can be checked to work. The idea is that [itex]f[/itex] can be chosen arbitrarily as initial condition in the orthogonal complement of [itex]\langle u\rangle[/itex].

Now, my real problem. Assume that [itex]u[/itex] is [itex]NM^2[/itex] component object, whose elements have been arranged so that first [itex]u=(u^1,\ldots, u^N)[/itex], and then each [itex]u^n[/itex] is a [itex]M\times M[/itex] matrix. Assume that [itex]g:\mathbb{R}^N\to\mathbb{C}^M[/itex] is some fixed function. Then we want to find [itex]f:\mathbb{R}^N\to\mathbb{C}^M[/itex] such that symbolically the same formula

[tex]
g(x) = u\cdot\nabla_x f(x)
[/tex]

holds. Now the previous solution formula doesn't work anymore, and I don't know how to fix the situation.

I am in particular interested in the case [itex]N=3,M=2[/itex] and

[tex]
u=\Big(
\left(\begin{array}{cc}
\frac{1}{\sqrt{2}} & \frac{1}{\sqrt{2}} \\
\frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}} \\
\end{array}\right),\;
\left(\begin{array}{cc}
\frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}} \\
-\frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}} \\
\end{array}\right),\;
\left(\begin{array}{cc}
0 & i \\
-i & 0 \\
\end{array}\right)\Big)
[/tex]

So this case is the same as

[tex]
g_1(x) = \frac{1}{\sqrt{2}}\big(\partial_1 f_1(x) + \partial_2 f_1(x) + \partial_1 f_2(x) - \partial_2 f_2(x) \big) + i \partial_3 f_2(x)
[/tex]
[tex]
g_2(x) = \frac{1}{\sqrt{2}}\big(\partial_1 f_1(x) - \partial_2 f_1(x) - \partial_1 f_2(x) - \partial_2 f_2(x)\big) - i \partial_3 f_1(x)
[/tex]

I don't know if this special case is any easier than the general case, though.
 
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  • #2
One possible way to start is to first consider the special case [itex]g=0[/itex]. The equation [itex]0=u\cdot\nabla_x f(x)[/itex] with my [itex]u[/itex] is the same as

[tex]
\left(\begin{array}{c}
\partial_3 f_1 \\ \partial_3 f_2 \\
\end{array}\right)
= -\frac{i}{\sqrt{2}}
\left(\begin{array}{cc}
\partial_1 - \partial_2 & -\partial_1 - \partial_2 \\
-\partial_1 - \partial_2 & -\partial_1 + \partial_2 \\
\end{array}\right)
\left(\begin{array}{c}
f_1 \\ f_2 \\
\end{array}\right)
[/tex]

Unfortunately, I was unable to learn anything about this equation either. A formal solution can be written as

[tex]
\left(\begin{array}{c}
f_1(x_1,x_2,x_3) \\ f_2(x_1,x_2,x_3) \\
\end{array}\right)
= \exp\Big(-\frac{ix_3}{\sqrt{2}}
\left(\begin{array}{cc}
\partial_1 - \partial_2 & -\partial_1 - \partial_2 \\
-\partial_1 - \partial_2 & -\partial_1 + \partial_2 \\
\end{array}\right)\Big)
\left(\begin{array}{c}
f_1(x_1,x_2,0) \\ f_2(x_1,x_2,0) \\
\end{array}\right)
[/tex]

Unfortunately, the matrices

[tex]
\left(\begin{array}{cc}
1 & -1 \\ -1 & -1 \\
\end{array}\right),\quad\quad
\left(\begin{array}{cc}
-1 & -1 \\ -1 & 1 \\
\end{array}\right)
[/tex]

cannot be diagonalized simultaneously, and therefore the exponential series cannot be written in terms of Taylor series after a linear transform. I cannot see what kind of transformation the series represents.
 
  • #3
jostpuur said:
If it is assumed that [itex]f,g[/itex] are differentiable, the formula can be checked to work.

I think we'll have to assume that the partial derivatives of [itex]g[/itex] are bounded too. This is probably not relevant for the discussion, since I'm interested in finding formulas mostly, but anyway, I wanted to fix the original claim...
 

1. What are derivatives in matrix elements?

Derivatives in matrix elements refer to the process of finding the rate of change of a matrix element with respect to one or more variables.

2. How are derivatives integrated in matrix elements?

To integrate derivatives in matrix elements, the variables are treated as constants and the integration is carried out using standard integration techniques.

3. Why is integrating derivatives in matrix elements important?

Integrating derivatives in matrix elements allows us to determine the behavior of a system over time and make predictions about its future state.

4. What are some applications of integrating derivatives in matrix elements?

Integrating derivatives in matrix elements is commonly used in fields such as physics, engineering, economics, and biology to model and analyze complex systems.

5. What are some challenges of integrating derivatives in matrix elements?

One of the main challenges of integrating derivatives in matrix elements is ensuring the accuracy and stability of the calculations, especially when dealing with large and complex systems.

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