NAG CL Interface f02fkc (real_symm_sparse_arnoldi)
Note:this function usesoptional parametersto define choices in the problem specification. If you wish to use default settings for all of the optional parameters, you need only read Sections 1 to 10 of this document. If, however, you wish to reset some or all of the settings this must be done by calling the option setting function f12fdc from the user-supplied function option. Please refer to Section 11 for a detailed description of the specification of the optional parameters.
The function may be called by the names: f02fkc or nag_eigen_real_symm_sparse_arnoldi.
3Description
f02fkc computes selected eigenvalues and the corresponding right eigenvectors of a real sparse symmetric matrix :
A specified number, , of eigenvalues , or the shifted inverses , may be selected either by largest or smallest modulus, largest or smallest value, or, largest and smallest values (both ends). Convergence is generally faster when selecting larger eigenvalues, smaller eigenvalues can always be selected by choosing a zero inverse shift (). When eigenvalues closest to a given value are required then the shifted inverses of largest magnitude should be selected with shift equal to the required value.
The sparse matrix is stored in symmetric coordinate storage (SCS) format. See Section 2.1.2 in the F11 Chapter Introduction.
f02fkc uses an implicitly restarted Arnoldi (Lanczos) iterative method to converge approximations to a set of required eigenvalues and corresponding eigenvectors. Further algorithmic information is given in Section 9 while a fuller discussion is provided in the F12 Chapter Introduction. If shifts are to be performed then operations using shifted inverse matrices are performed using a direct sparse solver.
4References
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore
HSL (2011) A collection of Fortran codes for large-scale scientific computation http://www.hsl.rl.ac.uk/
Lehoucq R B, Sorensen D C and Yang C (1998) ARPACK Users' Guide: Solution of Large-scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods SIAM, Philadelphia
5Arguments
1: – IntegerInput
On entry: , the order of the matrix .
Constraint:
.
2: – IntegerInput
On entry: the dimension of the array a. The number of nonzero elements in the lower triangular part of the matrix .
Constraint:
.
3: – const doubleInput
On entry: the array of nonzero elements of the lower triangular part of the symmetric matrix .
4: – const IntegerInput
5: – const IntegerInput
On entry: the row and column indices of the elements supplied in array a.
If and then is stored in . irow does not need to be ordered: an internal sort will force the correct ordering.
On entry: the number of eigenvalues to be computed.
Constraint:
.
7: – IntegerInput
On entry: the dimension of the array w.
The number of Arnoldi basis vectors to use during the computation.
At present there is no a priori analysis to guide the selection of ncv relative to nev. However, it is recommended that . If many problems of the same type are to be solved, you should experiment with increasing ncv while keeping nev fixed for a given test problem. This will usually decrease the required number of matrix-vector operations but it also increases the work and storage required to maintain the orthogonal basis vectors. The optimal ‘cross-over’ with respect to computation time is problem dependent and must be determined empirically.
Constraint:
.
8: – doubleInput
On entry: if the mode has been selected then sigma contains the real shift used; otherwise sigma is not referenced. This mode can be selected by setting the appropriate options in the user-supplied function option.
9: – function, supplied by the userExternal Function
monit is used to monitor the progress of f02fkc. monit may be specified as NULLFN if no monitoring is actually required. monit is called after the solution of each eigenvalue sub-problem and also just prior to return from f02fkc.
On entry: the dimension of the arrays w and rzest. The number of Arnoldi basis vectors used during the computation.
2: – IntegerInput
On entry: the number of the current Arnoldi iteration.
3: – IntegerInput
On entry: the number of converged eigenvalues so far.
4: – const doubleInput
On entry: the first nconv elements of w contain the converged approximate eigenvalues.
5: – const doubleInput
On entry: the first nconv elements of rzest contain the Ritz estimates (error bounds) on the converged approximate eigenvalues.
6: – Integer *Input/Output
On entry: set to zero.
On exit: if set to a nonzero value f02fkc returns immediately with NE_USER_STOP.
7: – Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to monit.
user – double *
iuser – Integer *
p – Pointer
The type Pointer will be void *. Before calling f02fkc you may allocate memory and initialize these pointers with various quantities for use by monit when called from f02fkc (see Section 3.1.1 in the Introduction to the NAG Library CL Interface).
10: – function, supplied by the userExternal Function
You can supply non-default options to the Arnoldi eigensolver by repeated calls to f12fdc from within option. (Please note that it is only necessary to call f12fdc; no call to f12fac is required from within option.) For example, you can set the mode to , you can increase the beyond its default and you can print varying levels of detail on the iterative process using .
If only the default options (including that the eigenvalues of largest magnitude are sought) are to be used then option may be specified as NULLFN.
On entry: contains details of the default option set. This array must be passed as argument icomm in any call to f12fdc.
On exit: contains data on the current options set which may be altered from the default set via calls to f12fdc.
2: – doubleCommunication Array
On entry: contains details of the default option set. This array must be passed as argument comm in any call to f12fdc.
On exit: contains data on the current options set which may be altered from the default set via calls to f12fdc.
3: – Integer *Input/Output
On entry: set to zero.
On exit: if set to a nonzero value f02fkc returns immediately with NE_USER_STOP.
4: – Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to option.
user – double *
iuser – Integer *
p – Pointer
The type Pointer will be void *. Before calling f02fkc you may allocate memory and initialize these pointers with various quantities for use by option when called from f02fkc (see Section 3.1.1 in the Introduction to the NAG Library CL Interface).
11: – Integer *Output
On exit: the number of converged approximations to the selected eigenvalues. On successful exit, this will normally be nev.
12: – doubleOutput
On exit: the first nconv elements contain the converged approximations to the selected eigenvalues.
13: – doubleOutput
Note: the dimension, dim, of the array v
must be at least
.
On exit: contains the eigenvectors associated with the eigenvalue
, for (stored in w). For eigenvalue, , the corresponding eigenvector is stored in
, for .
14: – IntegerInput
On entry: the stride separating, in the array v, the elements of a real eigenvector from the corresponding elements of the next eigenvector.
Constraint:
.
15: – doubleOutput
On exit: the residual for the estimates to the eigenpair and is returned in , for .
16: – Nag_Comm *
The NAG communication argument (see Section 3.1.1 in the Introduction to the NAG Library CL Interface).
17: – NagError *Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).
6Error Indicators and Warnings
NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument had an illegal value.
NE_BOTH_ENDS_1
The option has been set but only eigenvalue is requested.
NE_INT
On entry, . Constraint: .
On entry, .
Constraint: .
On entry, . Constraint: .
NE_INT_2
On entry, and . Constraint: .
On entry, and . Constraint: .
On entry, and . Constraint: .
On entry, and . Constraint: .
On entry, and .
Constraint: .
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
A serious error, code , has occurred in an internal call. Check all function calls and array sizes. If the call is correct then please contact NAG for assistance.
NE_INVALID_OPTION
The maximum number of iterations, through the optional parameter , has been set to a non-positive value.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_SINGULAR
On entry, the matrix is numerically singular and could not be inverted. Try perturbing the value of .
NE_SPARSE_COL
On entry, for , , . Constraint: .
NE_SPARSE_ROW
On entry, for , . Constraint: .
NE_TOO_MANY_ITER
The maximum number of iterations has been reached. The maximum number of iterations . The number of converged eigenvalues . See the function document for further details.
The relative accuracy of a Ritz value (eigenvalue approximation), , is considered acceptable if its Ritz estimate . The default value for is the machine precision given by X02AJC. The Ritz estimates are available via the monit function at each iteration in the Arnoldi process, or can be printed by setting option to a positive value.
8Parallelism and Performance
f02fkc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f02fkc 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 function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.
9Further Comments
f02fkc calls functions based on the ARPACK suite in Chapter F12. These functions use an implicitly restarted Lanczos iterative method to converge to approximations to a set of required eigenvalues (see the F12 Chapter Introduction).
In the default mode, only matrix-vector multiplications are performed using the sparse matrix during the Lanczos process; f11xec can be used to perform this task. Each iteration is, therefore, cheap computationally, relative to the alternative, , mode described below. It is most efficient (i.e., the total number of iterations required is small) when the eigenvalues of largest magnitude are sought and these are distinct.
Although there is an option for returning the smallest eigenvalues using this mode (see option), the number of iterations required for convergence will be far greater or the method may not converge at all. However, where convergence is achieved, mode may still prove to be the most efficient since no inversions are required. Where smallest eigenvalues are sought and mode is not suitable, or eigenvalues close to a given real value are sought, the mode should be used.
If the mode is used (via a call to f12fdc in option) then the matrix is used in linear system solves by the Lanczos process. This is first factorized internally using a direct sparse factorization under the assumption that the matrix is indefinite. If the factorization determines that the matrix is numerically singular then the function exits with an error. In this situation it is normally sufficient to perturb by a small amount and call f02fkc again. After successful factorization, subsequent solves are performed by backsubstitution using the sparse factorization.
Finally, f02fkc transforms the eigenvectors. Each eigenvector is normalized so that .
The monitoring function monit provides some basic information on the convergence of the Lanczos iterations. Much greater levels of detail on the Lanczos process are available via option . If this is set to a positive value then information will be printed, by default, to standard output. The destination of monitoring information can be changed using the option.
9.1Additional Licensor
The direct sparse factorization is performed by an implementation of HSL_MA97 (see HSL (2011)).
10Example
This example solves in mode, where is obtained from the standard central difference discretization of the one-dimensional Laplacian operator on , with zero Dirichlet boundary conditions.
Internally f02fkc calls functions from the suite f12fac,f12fbc,f12fcc,f12fdcandf12fec. Several optional parameters for these computational functions define choices in the problem specification or the algorithm logic. In order to reduce the number of formal arguments of f02fkc these optional parameters are also used here and have associated default values that are usually appropriate. Therefore, you need only specify those optional parameters whose values are to be different from their default values.
Optional parameters may be specified via the user-supplied function option in the call to f02fkc. option must be coded such that one call to f12fdc is necessary to set each optional parameter. All optional parameters you do not specify are set to their default values.
The remainder of this section can be skipped if you wish to use the default values for all optional parameters.
The following is a list of the optional parameters available. A full description of each optional parameter is provided in Section 11.1.
For each option, we give a summary line, a description of the optional parameter and details of constraints.
The summary line contains:
the keywords, where the minimum abbreviation of each keyword is underlined;
a parameter value,
where the letters , and denote options that take character, integer and real values respectively;
the default value, where the symbol is a generic notation for machine precision (see X02AJC).
Keywords and character values are case and white space insensitive.
Optional parameters used to specify files (e.g., and ) have type Integer. This Integer value corresponds with the Nag_FileID as returned by x04acc. See Section 10 for an example of the use of this facility.
Advisory
Default
(See Section 3.1.1 in the Introduction to the NAG Library CL Interface for further information on NAG data types.)
If the optional parameter is set then optional parameter specifications are listed in a file by setting the option to a file identification (unit) number associated with advisory messages (see x04acc).
Defaults
This special keyword may be used to reset all optional parameters to their default values.
Iteration Limit
Default
The limit on the number of Lanczos iterations that can be performed before f12fbc exits. If not all requested eigenvalues have converged to within and the number of Lanczos iterations has reached this limit then f12fbc exits with an error; f12fcc can still be called subsequently to return the number of converged eigenvalues, the converged eigenvalues and, if requested, the corresponding eigenvectors.
LargestMagnitude
Default
Both Ends
LargestAlgebraic
SmallestAlgebraic
SmallestMagnitude
The Lanczos iterative method converges on a number of eigenvalues with given properties. The default is for f12fbc to compute the eigenvalues of largest magnitude using . Alternatively, eigenvalues may be chosen which have part, , or part; or eigenvalues which are from of the algebraic spectrum.
Nolist
Default
List
Optional parameter enables printing of each optional parameter specification as it is supplied. suppresses this printing.
Monitoring
Default
(See Section 3.1.1 in the Introduction to the NAG Library CL Interface for further information on NAG data types.)
Unless is set to (the default), monitoring information is output to Nag_FileID during the solution of each problem; this may be the same as . The type of information produced is dependent on the value of , see the description of the optional parameter in this section for details of the information produced. Please see x04acc to associate a file with a given Nag_FileID.
PrintLevel
Default
This controls the amount of printing produced by f02fkc as follows.
Output
No output except error messages. If you want to suppress all output, set .
The set of selected options.
Problem and timing statistics on final exit from f12fbc.
A single line of summary output at each Lanczos iteration.
If
is set, then at each iteration, the length and additional steps of the current Lanczos factorization and the number of converged Ritz values; during re-orthogonalization, the norm of initial/restarted starting vector; on a final Lanczos iteration, the number of update iterations taken, the number of converged eigenvalues, the converged eigenvalues and their Ritz estimates.
Problem and timing statistics on final exit from f12fbc. If
is set,
then at each iteration, the number of shifts being applied, the eigenvalues and estimates of the symmetric tridiagonal matrix , the size of the Lanczos basis, the wanted Ritz values and associated Ritz estimates and the shifts applied; vector norms prior to and following re-orthogonalization.
If
is set,
then on final iteration, the norm of the residual; when computing the Schur form, the eigenvalues and Ritz estimates both before and after sorting; for each iteration, the norm of residual for compressed factorization and the symmetric tridiagonal matrix ; during re-orthogonalization, the initial/restarted starting vector; during the Lanczos iteration loop, a restart is flagged and the number of the residual requiring iterative refinement; while applying shifts, some indices.
If
is set,
then during the Lanczos iteration loop, the Lanczos vector number and norm of the current residual; while applying shifts, key measures of progress and the order of ; while computing eigenvalues of , the last rows of the Schur and eigenvector matrices; when computing implicit shifts, the eigenvalues and Ritz estimates of .
If is set, then during Lanczos iteration loop: norms of key components and the active column of , norms of residuals during iterative refinement, the final symmetric tridiagonal matrix ; while applying shifts: number of shifts, shift values, block indices, updated tridiagonal matrix ; while computing eigenvalues of : the diagonals of , the computed eigenvalues and Ritz estimates.
Note that setting can result in very lengthy output.
Regular
Default
RegularInverse
ShiftedInverse
These options define the computational mode which in turn defines the form of operation to be performed.
where is real
Tolerance
Default
An approximate eigenvalue has deemed to have converged when the corresponding Ritz estimate is within relative to the magnitude of the eigenvalue.