How can e^{Diag Matrix} not be an infinite series?

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The discussion centers on the mathematical concept of the matrix exponential, specifically how e^{\Lambda t} can be expressed as a single matrix with e^{\lambda_i t} on its diagonal. The participants explain that e^{At} \vec{u}(0) = \vec{u}(t) serves as a solution to differential equations using eigenvectors. The series expansion of the exponential function is detailed, demonstrating that each diagonal entry corresponds to an infinite series converging to e^{t \lambda_j}. This understanding is crucial for applying eigenvalues and eigenvectors in solving differential equations.

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kostoglotov
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So, in a section on applying Eigenvectors to Differential Equations (what a jump in the learning curve), I've encountered

e^{At} \vec{u}(0) = \vec{u}(t)

as a solution to certain differential equations, if we are considering the trial substitution y = e^{\lambda t} and solving for constant coefficients. I understand the idea more or less, but the math gets quite involved, particularly with complex eigenvalues and eigenvectors.

I can see how (or at least I can follow and accept the explanation for) e^{At} being an infinite series.

But how does e^{\Lambda t} = a single matrix with e^{\lambda_i t} on it's diagonal?
 
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If we write the series expansion of the exponential, we get

## e^{\Lambda t} = \mathbf{1} + \Lambda t + \frac{1}{2}t^2 \Lambda^2 + \cdots ##,

where ## \mathbf{1} ## is the identity matrix. Now ## \Lambda ## is a diagonal matrix, so the result of multiplying it by itself is simple, e.g., ## \Lambda^2 = diag(\lambda_1^2, ..., \lambda_n^2)##. Therefore,

## e^{\Lambda t} = \mathbf{1} + diag(\lambda_1, ...,\lambda_n) t + \frac{1}{2}t^2 diag(\lambda_1^2,...,\lambda_n^2) + \cdots ##.

If you write this out in matrix form, summing to form a single matrix, you will see that each entry in the resulting diagonal is an infinite series that itself equals ## e^{t \lambda_j } ## for the ## jj ## matrix entry.
 
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It works because for ##D = \mathrm{diag}(d_1, d_2, \dots d_N)##, ##D^n = \mathrm{diag}(d_1^n, d_2^n, \dots d_N^n)##.

Looks like Geofleur beat me to it :smile:
 

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