Solution to Matrix Differential Equation

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SUMMARY

The discussion focuses on solving a system of differential equations represented in matrix form, specifically the equation \(\dot{\vec{x}}(t) = A \vec{x}(t)\), where \(A\) is a 2x2 matrix with time-dependent elements. The solution involves transforming the system into a diagonal form \(\dot{\vec{x}}(t) = D \vec{x}(t)\), where \(D\) contains the eigenvalues of \(A\). This transformation requires finding an invertible matrix \(P\) composed of the eigenvectors of \(A\). Understanding this process is crucial for effectively solving the system of differential equations.

PREREQUISITES
  • Matrix algebra, specifically eigenvalues and eigenvectors
  • Understanding of differential equations and their solutions
  • Linear algebra concepts, including matrix similarity
  • Basic calculus for integration and differentiation
NEXT STEPS
  • Study eigenvalues and eigenvectors in depth using linear algebra resources
  • Learn about diagonalization of matrices and its applications in differential equations
  • Explore methods for solving systems of differential equations, focusing on matrix methods
  • Practice transforming systems of differential equations into diagonal form
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Students and professionals in mathematics, engineering, and physics who are working with systems of differential equations and require a solid understanding of linear algebra concepts.

WolfOfTheSteps
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How do I write the form of the solution to this equation:

<br /> <br /> \dot{\vec{x}}(t) =<br /> \left [ \begin{array}{cc}<br /> a_{11}(t) &amp; a_{12}(t) \\<br /> a_{21}(t) &amp; a_{22}(t)<br /> \end{array} \right ] \vec{x}(t)<br /> <br />


I just need to be able to write x1(t) and x2(t) so I can do the rest of the problem I'm working on. Getting this would just be a small step in my solution, but I am very rusty with my differential equations! :(

Initially, I thought to write:

<br /> x_1(t) = \int_{t_0}^{t}x_1(\tau)a_{11}(\tau) + x_2(\tau)a_{12}(\tau)d\tau <br />


<br /> x_2(t) = \int_{t_0}^{t}x_1(\tau)a_{21}(\tau) + x_2(\tau)a_{22}(\tau)d\tau <br />

But that has the solutions with dependence on x1(t) and x2(t). That's not the way to write it, is it?

Thanks.
 
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WolfOfTheSteps said:
How do I write the form of the solution to this equation:

<br /> <br /> \dot{\vec{x}}(t) =<br /> \left [ \begin{array}{cc}<br /> a_{11}(t) &amp; a_{12}(t) \\<br /> a_{21}(t) &amp; a_{22}(t)<br /> \end{array} \right ] \vec{x}(t)<br /> <br />


I just need to be able to write x1(t) and x2(t) so I can do the rest of the problem I'm working on. Getting this would just be a small step in my solution, but I am very rusty with my differential equations! :(

Initially, I thought to write:

<br /> x_1(t) = \int_{t_0}^{t}x_1(\tau)a_{11}(\tau) + x_2(\tau)a_{12}(\tau)d\tau <br />


<br /> x_2(t) = \int_{t_0}^{t}x_1(\tau)a_{21}(\tau) + x_2(\tau)a_{22}(\tau)d\tau <br />

But that has the solutions with dependence on x1(t) and x2(t). That's not the way to write it, is it?

Thanks.

Let's look at your system of DEs this way:
<br /> \dot{\vec{x}}(t) = A \vec{x}(t)<br />

What you'd like is a system that looks like this:
<br /> \dot{\vec{x}}(t) = D \vec{x}(t)<br />
where D is a diagonal matrix.

This will untangle things so that you have x1'(t) = d11 x1(t) and x2'(t) = d22 x2(t).

These are easy to solve, since each one involves only a single variable.

Getting the matrix D is the hard part, though, since doing this involves changing to a different basis (for R2 in your case). Without going into too many details, you'll want to find a matrix D that is similar to your original matrix A, which I'm assuming is invertible. Similarity is precisely defined this way: If A ~ D, then for some invertible matrix P, AP = PD.

Equivalently, P^{-1}AP = P^{-1}PD = D.

You will need to come up with a matrix P whose columns are the new basis, and a matrix P^{-1}, the inverse of P.

To wind this up, the columns of P are the eigenvectors of matrix A, and it turns out that the diagonal entries of D are the eigenvalues of A.

I hope I've given you enough to at least get you started searching for the things to learn more about. Solving a system of DEs, even the simplest possible system in two variables requires a significant amount of understanding in linear algebra.

Mark
 
Mark44 said:
Let's look at your system of DEs this way:
<br /> \dot{\vec{x}}(t) = A \vec{x}(t)<br />

What you'd like is a system that looks like this:
<br /> \dot{\vec{x}}(t) = D \vec{x}(t)<br />
where D is a diagonal matrix.

This will untangle things so that you have x1'(t) = d11 x1(t) and x2'(t) = d22 x2(t).

These are easy to solve, since each one involves only a single variable.

Getting the matrix D is the hard part, though, since doing this involves changing to a different basis (for R2 in your case). Without going into too many details, you'll want to find a matrix D that is similar to your original matrix A, which I'm assuming is invertible. Similarity is precisely defined this way: If A ~ D, then for some invertible matrix P, AP = PD.

Equivalently, P^{-1}AP = P^{-1}PD = D.

You will need to come up with a matrix P whose columns are the new basis, and a matrix P^{-1}, the inverse of P.

To wind this up, the columns of P are the eigenvectors of matrix A, and it turns out that the diagonal entries of D are the eigenvalues of A.

I hope I've given you enough to at least get you started searching for the things to learn more about. Solving a system of DEs, even the simplest possible system in two variables requires a significant amount of understanding in linear algebra.

Mark

Yes, my linear algebra is a few years out of service, but it is not non-existent. You have refreshed my memory. Thanks.
 

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