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Spectral radius of a matrix
This functionality does not run in MATLAB.
numeric::spectralRadius(A, <x_{0}>, <n>, <mode>, <ReturnType = t>, <NoWarning>)
numeric::spectralRadius(A) returns data corresponding to the eigenvalue of the matrix A that has the largest absolute value.
numeric::spectralRadius and numeric::spectralradius are equivalent.
The spectral radius of a matrix with eigenvalues λ_{i} is max(|λ_{i}|).
The return value lambda is an approximation of the corresponding eigenvalue: abs(lambda) is the spectral radius.
The return value x is the corresponding normalized eigenvector: .
The return value provides an error estimate for the eigenvalue. For Hermitian matrices this is a rigorous upper bound for the error |lambda - λ_{exact}|, where λ_{exact} is the exact eigenvalue.
numeric::spectralRadius implements the power method to compute the eigenvalue and the associated eigenvector defining the spectral radius: the vector iteration "converges" towards the eigenspace associated with the spectral radius. The starting vector x_{0} is provided by the second argument of numeric::spectralradius. If no starting vector is provided by the user, a randomly chosen vector is used.
Note: The iteration does not converge (converges slowly), if the spectral radius is generated by several distinct eigenvalues with the same (similar) absolute value. |
Internally, the iteration stops, when the approximation of the eigenvalue becomes stationary within the relative precision given by DIGITS. If this does not happen within n iterations, then a warning is issued and the present values are returned. Cf. Example 4.
The function is sensitive to the environment variable DIGITS, which determines the numerical working precision.
We let the routine choose a random starting vector:
A := matrix(2, 2, [[10, 1], [1, 20]]): numeric::spectralradius(A)
We define a starting vector as a 1-dimensional array and allow a maximum of 1000 internal iterations:
A := array(1..2, 1..2, [[1, 2], [5, -10]]): x0 := array(1..2, [1, 1]): numeric::spectralradius(A, x0, 1000)
Next, we use a list to specify a starting vector:
A := array(1..2, 1..2, [[I, 3], [3, I]]): numeric::spectralradius(A, [1, 1], 1000)
delete A, x0:
With the default setting of DIGITS = 10, the following result is computed using HardwareFloats.
A := hfarray(1..2, 1..2, [[10^4, 10^4], [50, 60]]): x0 := array(1..2, [1, 1]): numeric::spectralradius(A, x0)
We request SoftwareFloats in the next call. Note the difference in the trailing digits:
numeric::spectralradius(A, x0, Soft)
delete DIGITS, A, x0:
The eigenvector that is returned can have various types. If no starting vector is provided, the type of the matrix determines the type of the eigenvector:
A:= array(1..2, 1..2, [[1, 2], [3, 4]]): [l, x, residue]:= numeric::spectralradius(A);
domtype(x)
A:= hfarray(1..2, 1..2, [[1, 2], [3, 4]]): [l, x, residue]:= numeric::spectralradius(A): domtype(x)
A:= matrix(2, 2, [[1, 2], [3, 4]]): [l, x, residue]:= numeric::spectralradius(A): domtype(x)
If a starting vector is provided, its type determines the type of the return vector:
A:= hfarray(1..2, 1..2, [[1, 2], [3, 4]]): x0:= [1, 1]: [l, x, residue]:= numeric::spectralradius(A, x0): domtype(x)
x0:= array(1..2, [1, 1]): [l, x, residue]:= numeric::spectralradius(A, x0): domtype(x)
x0:= hfarray(1..2, [1, 1]): [l, x, residue]:= numeric::spectralradius(A, x0): domtype(x)
x0:= matrix([1, 1]): [l, x, residue]:= numeric::spectralradius(A, x0): domtype(x)
The return type can be requested explicitly:
[l, x, residue]:= numeric::spectralradius(A, x0, ReturnType = DOM_LIST): domtype(x)
[l, x, residue]:= numeric::spectralradius(A, x0, ReturnType = DOM_HFARRAY): domtype(x)
delete A, x0, l, x, residue:
The following matrix has two distinct eigenvalues 1 and -1 of the same absolute value. The power method must fail.
A := array(1..2, 1..2, [[1, 0], [0, -1]]):
We allow a maximum of 1000 internal steps. The call results in a warning. The large residue also indicates that the power method did not converge:
numeric::spectralradius(A, [1, 1], 1000)
Warning: There is no convergence of vector iteration. [numeric::spectralradius]
delete A:
A |
An m×m array of domain type DOM_ARRAY or DOM_HFARRAY or a matrix of category Cat::Matrix |
x_{0} |
A starting vector: a 1-dimensional array, or an hfarray, or a list of length m. Also 2-dimensional arrays (array(1..m, 1..1, ...), hfarray(1..m, 1..1, ...)) and matrices representing vectors are accepted. |
n |
The maximal number of iterations: a positive integer. The default value is 1000. |
mode |
One of the flags Hard, HardwareFloats, Soft, or SoftwareFloats |
Hard, HardwareFloats, Soft, SoftwareFloats |
With Hard (or HardwareFloats), computations are done using fast hardware float arithmetic from within a MuPAD^{®} session. Hard and HardwareFloats are equivalent. With this option, the input data are converted to hardware floats and processed by compiled C code. The result is reconverted to MuPAD floats and returned to the MuPAD session. With Soft (or SoftwareFloats) computations are dome using software float arithmetic provided by the MuPAD kernel. Soft and SoftwareFloats are equivalent. SoftwareFloats is used by default if the current value of DIGITS is larger than 15 and the input matrix A is not of domain type DOM_HFARRAY. Compared to the SoftwareFloats used by the MuPAD kernel, the computation with HardwareFloats may be many times faster. Note, however, that the precision of hardware arithmetic is limited to about 15 digits. Further, the size of floating-point numbers may not be larger than approximately 10^{308} and not smaller than approximately 10^{- 308}. If no HardwareFloats or SoftwareFloats are requested explicitly, the following strategy is used: If the current value of DIGITS is smaller than 16 or if the matrix A is a hardware float array of domain type DOM_HFARRAY, then hardware arithmetic is tried. If this is successful, the result is returned. If the result cannot be computed with hardware floats, software arithmetic by the MuPAD kernel is tried. If the current value of DIGITS is larger than 15 and the input matrix A is not of domain type DOM_HFARRAY, or if one of the options Soft, SoftwareFloats or Symbolic is specified, MuPAD computes the result with its software arithmetic without trying to use hardware floats first. There may be several reasons for hardware arithmetic to fail:
If neither HardwareFloats nor SoftwareFloats is specified, the user is not informed whether hardware floats or software floats are used. If HardwareFloats are specified but fail due to one of the reasons above, a warning is issued that the (much slower) software floating-point arithmetic of the MuPAD kernel is used. Note that HardwareFloats can only be used if all input data can be converted to floating-point numbers. The trailing digits in floating-point results computed with HardwareFloats and SoftwareFloats may differ. |
ReturnType |
Option, specified as ReturnType = t Return the eigenvector associated with the spectral radius as a vector of domain type t. The following return types are available: DOM_ARRAY, or DOM_HFARRAY, or DOM_LIST, or Dom::Matrix(), or Dom::DenseMatrix(). |
NoWarning |
Suppresses warnings |
A list [lambda, x, residue] is returned. The floating-point number lambda is an approximation of the eigenvalue of largest absolute value. The vector x is a numerical eigenvector corresponding to lambda. residue is a floating-point number indicating the numerical quality of lambda and x.
If no return type is requested via the ReturnType option, the type of the returned vector x coincides with the type of the input vector x_{0} (i.e., it is a 1-dimensional array of type DOM_ARRAY or DOM_HFARRAY, respectively, or a list, or a column vector of type matrix or densematrix. If no starting vector is specified, the type of x is determined by the type of A.