How to Solve the Exponential of a Matrix?

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The exponential of a matrix can be defined using a Taylor series, similar to how it is done for real numbers, expressed as e^(At) = Σ (A^n t^n) / n!. For small values of t, this series can be truncated to e^(At) ≈ I + At, where I is the identity matrix. The Cayley-Hamilton theorem indicates that a matrix satisfies its own characteristic equation, allowing any power of the matrix to be expressed as a linear combination of the identity matrix and the matrix itself. This method is commonly applied in fields like electrical engineering. Understanding these concepts is essential for deriving related equations and functions.
Sherin
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Please help me understand the following step

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When ## a ## is just a real number, one can use a Taylor series to represent ## e^{at} ## as

## e^{at} = \sum_{n=0}^{\infty} \frac{(at)^n}{n!} = 1 + at + \frac{1}{2}(at)^2 + \ldots ##

By analogy, one can define the exponential of ## \mathbf{A}t ##, where ## \mathbf{A} ## is now a matrix, as

## e^{\mathbf{A}t} = \sum_{n=0}^{\infty} \frac{(\mathbf{A}t)^n}{n!} ##.

Because multiplying a matrix by itself is perfectly well defined, the above sum makes sense. Now if ## t ## is not too large, we can truncate the series and write

## e^{\mathbf{A}t} \approx (\mathbf{A}t)^0 + \mathbf{A}t \equiv \mathbf{I} + \mathbf{A}t ##,

where ##\mathbf{I}## is the identity matrix. Using this truncated sum, your formula can be derived, except for the time dependent functions ##\alpha_1(t)## and ##\alpha_2(t)##. Perhaps someone else can shed some light on where those might be coming from. What is the context in which you are seeing this equation?
 
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Geofleur's infinite series is the place to start. The trick is to use the Cayley-Hamilton theorem, which tells you that a matrix satisfies it's own characteristic equation. Sine you have a 2x2 matrix this is a second order polynomial, so you can write ##\mathbf{A}^2=\alpha_0 \mathbf{I} + \alpha_1 \mathbf{A}## for some ##\alpha_0## and ##\alpha_1## . It follows that any power of your matrix can also be represented by a linear combination of ##\mathbf{I}## and ## \mathbf{A}##. Hopefully that shows you where the result comes from.

This is a common approach used in electrical engineering.

Jason
 
Thank you so much for your help!
 

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