The matrix exponential of a square matrix is defined by the power series
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(1)
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(2)
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(3)
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which converges for every square matrix , where
is the identity matrix.
The matrix exponential is implemented in the Wolfram
Language as MatrixExp[m].
The Kronecker sum satisfies the nice property
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(4)
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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).
For a constant matrix , the system
has solution
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(5)
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If
is a Hermitian matrix and
is real, the propagator is
a unitary matrix and preserves the usual inner
product. This construction is used for a finite vector
basis approximation to the quartic oscillator
(Trott 2006). Polynomial approximations to this exponential have been developed using
Hermite polynomials and Gegenbauer
polynomials (Vijay et al. 1999, Vijay and Metiu 2002). Parity-time
symmetry alone does not give this unitarity property in the usual inner
product.
In some cases, it is a simple matter to express the matrix exponential. For example, when
is a diagonal matrix, exponentiation
can be performed simply by exponentiating each of the diagonal elements. For example, given a diagonal
matrix
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(6)
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the matrix exponential is given by
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(7)
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When a matrix is diagonalizable, its exponential can be computed by first diagonalizing it.
When
is a nilpotent matrix, the exponential is given
by a matrix polynomial because some power
of
vanishes. For example, when
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(8)
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then
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(9)
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and .
For the zero matrix ,
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(10)
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i.e., the identity matrix. In general,
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(11)
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so the exponential of a matrix is always invertible, with inverse the exponential of the negative of the matrix. However, in general, the formula
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(12)
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holds whenever
and
commute, i.e.,
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(13)
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The identity need not hold when and
do not commute. For example,
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(14)
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while
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(15)
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Even for a general real matrix, however, the matrix exponential can be quite
complicated:
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(16)
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where
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(17)
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(18)
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(19)
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(20)
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and .
As
,
this becomes
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(21)
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