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Return to Part VI of the course APMA0330
This section presents the residue method and its application to determination of the inverse Laplace transforms. Although the residue technique is widely used in analysis and theory of functions of a complex variable, its presentation is based on the novel approach developed by Vladimir Dobrushkin. It embraces previously discussed methods: partial fraction decomposition and the convolution formula.
Now we define the inverse Laplace transform using the residue method, assuming that singular points are at most of
multiplicity 3. It is applied to
irreducible expressions of the form: F(λ) = P(λ)/Q(λ), where the degree of Q(λ) must be greater than that of P(λ). Zeroes of the denominator polynomial are called poles of the meromorphic functionF(λ).
Then the inverse Laplace transform of the meromorphic function F(λ) is the sum of all residues of \( e^{\lambda t} F(\lambda ) \) evaluated at the poles of F(λ).
where the sum ranges over all zeroes of the equation \( Q(\lambda ) =0 , \) and the function
f must be multiplied by the Heaviside function to ensure that f(t) is identically zero for a negative argument.
The residues at each null are evaluated according to the following rule.
Suppose that the meromorphic function\( F(\lambda ) = \frac{P(\lambda )}{Q(\lambda )} \) is a ratio
of two irreducible polynomials (or entire functions). Let \( \lambda = \lambda_i , \ i=1,2,\ldots , N, \)
be null of the denominator (\( N = \infty \) if Q(λ) is an entire function).
Recall that if \( Q(\lambda_i ) =0, Q' (\lambda_i ) =0, \ldots , Q^{(m_i -1)} (\lambda_i ) =0,
Q^{(m_i )} (\lambda_i )\ne 0 , \) then we say that the denominator has zero at \( \lambda = \lambda_i \)
of multiplicitymi. In this case, we say that the function F(λ) has poles at
\( \lambda = \lambda_i , \quad i=1,2,\ldots , N . \) Note that the numerator is not zero at
these poles.
If \( \lambda = \lambda_i \) is a simple root (meaning that its multiplicity is 1)
of the equation \( Q(\lambda ) =0 , \) then
Note that if \( \lambda_i = \alpha + {\bf j} \beta \) is a complex null of the denominator
Q(λ) (the root of the polynomial equation with real coefficients \( Q(\lambda ) =0 \) ),
then \( \overline{\lambda_i} = \alpha - {\bf j} \beta \) is also a null of Q(λ).
In this case, we don't need to calculate the residue at the complex conjugate because we know that the output will be a complex conjugate
of the residue at \( \alpha + {\bf j} \beta .\)
Therefore, if the denominator Q(λ) has two complex conjugate roots
\( \lambda = \alpha \pm {\bf j} \beta , \) then the sum of residues at these points is just
double value of the real part of one of them:
Assuming[ t > 0, Exp[s*t]*(2*s - 1)/2/s/(s - 2)^3 /. s -> I]
(7/250 - (12 I)/125) E^(I t)
We don't need to evaluate the residue at λ = −j because its value is a complex conjugate of the residure at λ = j. So their sum is just real part times 2:
Now, we have two double complex conjugate roots \( \lambda = \pm {\bf j} . \) The denominator Q(λ) = (λ² + 1)²(λ - 2)³ also has a triple real root λ = 2. The first step is to define the denominator Q(λ) and the numerator P(λ) = 2λ −1 in Mathematica:
s /. Solve[Q == 0] (* Finds the roots of Q(s)=0 *)
Out[3]= {-I, -I, I, I, 2, 2, 2}
Tally[%] (* Finds the number of repeats of each root (multiplicity)*)
Out[4]= {{-I, 2}, {I, 2}, {2, 3}}
MatrixForm[%] (* Creates solutions matrix with roots in one column and multiplicities in another *)
Out[5]=
(-i 2 )
( i 2 )
( 2 3 )
Dimensions[%] (* Measures the dimensions of the matrix (2x2),(3x2),(6x2), etc*)
Out[6]= {3, 2}
v = Part[%, 1] (* Finds the length of the matrix (v=number of rows) *)
Out[7]= 3
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