The real number is expressed as where and . Then . The integer power is found using a combination of binary powering and, if necessary, matrix inversion. The fractional power is computed, entirely in real arithmetic, using a real Schur decomposition and a Padé approximant.
Higham N J (2008) Functions of Matrices: Theory and Computation SIAM, Philadelphia, PA, USA
Higham N J and Lin L (2011) A Schur–Padé algorithm for fractional powers of a matrix SIAM J. Matrix Anal. Appl.32(3) 1056–1078
Higham N J and Lin L (2013) An improved Schur–Padé algorithm for fractional powers of a matrix and their Fréchet derivatives SIAM J. Matrix Anal. Appl.34(3) 1341–1360
1: – IntegerInput
On entry: , the order of the matrix .
2: – Real (Kind=nag_wp) arrayInput/Output
Note: the second dimension of the array a
must be at least
On entry: the matrix .
On exit: the matrix th power, .
3: – IntegerInput
On entry: the first dimension of the array a as declared in the (sub)program from which f01eqf is called.
4: – Real (Kind=nag_wp)Input
On entry: the required power of .
5: – IntegerInput/Output
On entry: ifail must be set to , or to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of means that an error message is printed while a value of means that it is not.
If halting is not appropriate, the value or is recommended. If message printing is undesirable, then the value is recommended. Otherwise, the value is recommended. When the value or is used it is essential to test the value of ifail on exit.
On exit: unless the routine detects an error or a warning has been flagged (see Section 6).
6Error Indicators and Warnings
If on entry or , explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
has eigenvalues on the negative real line. The principal th power is not defined. f01fqf can be used to find a complex, non-principal th power.
is singular so the th power cannot be computed.
has been computed using an IEEE double precision Padé approximant, although the arithmetic precision is higher than IEEE double precision.
An unexpected internal error occurred. This failure should not occur and suggests that the routine has been called incorrectly.
On entry, . Constraint: .
On entry, and . Constraint: .
An unexpected error has been triggered by this routine. Please
See Section 7 in the Introduction to the NAG Library FL Interface for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.
Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.
For positive integer , the algorithm reduces to a sequence of matrix multiplications. For negative integer , the algorithm consists of a combination of matrix inversion and matrix multiplications.
For a normal matrix (for which ) and non-integer , the Schur decomposition is diagonal and the algorithm reduces to evaluating powers of the eigenvalues of and then constructing using the Schur vectors. This should give a very accurate result. In general however, no error bounds are available for the algorithm.
8Parallelism and Performance
Background information to multithreading can be found in the Multithreading documentation.
f01eqf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f01eqf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.
The cost of the algorithm is . The exact cost depends on the matrix but if then the cost is independent of .
of real allocatable memory is required by the routine.
If estimates of the condition number of are required then f01jef should be used.