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(Horn and Johnson 1994, p. 208). Matrix exponentials are important in the solution of systems of ordinary differential equations (e.g., Bellman 1970). Since most matrices are diagonalizable, it is easiest to diagonalize the matrix before exponentiating it.
For a square matrix 𝐴 and positive integr π‘˜, the π‘˜th power of 𝐴 is defined by multiplying this matrix by itself repeatedly. That is, 𝐴 to the π‘˜th power is equal to 𝐴 multiplied by 𝐴 multiplied by 𝐴, and so on, multiplied by 𝐴, where there are π‘˜ instances of the matrix 𝐴.
If P is a projection matrix (i.e. is idempotent: P2 = P), its matrix exponential is: eP = I + (e 1)P.
Algorithm for Solving the System of Equations Using the Matrix Exponential We first find the eigenvalues of the matrix (linear operator) Calculate the eigenvectors and (in the case of multiple eigenvalues) generalized eigenvectors;
We mention a drawback of matrix exponentials. In general . e A + B e A e B . The trouble is that matrices do not commute, that is, in general .
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Yes, the exponential of a matrix can be defined by that Taylor series, and it is a very useful thing. See e.g. Wikipedia The proof that it converges is not difficult, using any sub-multiplicative matrix norm. If β€–β€– is such a norm, then any power series ncnAn converges whenever the real series n|cn|β€–Aβ€–n converges.
We mention a drawback of matrix exponentials. In general . e A + B e A e B . The trouble is that matrices do not commute, that is, in general .
Powers of a matrix We can raise square matrices to any (positive) power in the same way: if we want to get the cube of A, or A 3 A^3 A3, we multiply the matrix by itself 3 times, if we want A 4 A^4 A4, we multiply it by itself 4 times, and so on.

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