Section 4.1 Definition of \(\exp(A)\)
Definition 4.1.1.
For any \(n \times n\) matrix \(A\text{,}\) the matrix \(\exp(A)\) or \(e^A\) is called the matrix exponential of \(A\) and is calculated by
Note that the sum above always converges.
We'll be interested in \(2 \times 2\) matrices \(A\) and the exponential of the form \(\exp(tA)\text{.}\)
Theorem 4.1.2. \(\exp(tA)\) for diagonalizable \(A\).
Suppose \(A\) is diagonalizable as \(A = VDV^{-1}\text{.}\) Then,
Furthermore, if \(D = \bmat a00b\text{,}\) then
Proof.
We have that
For the second part, note that if \(D = \bmat a00b\text{,}\) then
so,
We don't need \(A\) to be diagonalizable in order for \(\exp(tA)\) to exist. When \(A\) is not diagonalizable, try to find a general form of \(A^k\text{.}\)
Example 4.1.3. Calculating \(\exp(tA)\) for \(A = \bmat0100\).
Note that
so \(A^k = \bmat0000\) for all \(k \geq 2\text{.}\) In calculating \(\exp(tA)\text{,}\) we get that
A very intriguing property of \(e^{tA}\) is that
Question 4.1.4. Why does this occur?
Take a look at the series representation of \(e^{tA}\) and differentiate it with respect to \(t\text{:}\)
So, \(\mathbf x(t) = e^{tA} \mathbf x_0\) is the general solution of \(\mathbf x' = A \mathbf x\) with \(\mathbf x(0) = \mathbf x_0\text{.}\)