Subroutine mld_precset

call mld_precset(p,what,val,info)

This routine sets the parameters defining the preconditioner. More precisely, the parameter identified by what is assigned the value contained in val.

The routine may also be invoked as a method of the preconditioner object as in the following:

call p%set(what,val,info [,ilev, pos])

In this case it is also possible to specify an optional ilev argument that restricts the effect of the call to the specified level.

Finally, if the user has developed a new type of smoother and/or solver by extending one of the base MLD2P4 types, and has declared a variable of the new type in the main program, it is possible to pass the new smoother/solver variable to the setup routine as follows:

call p%set(smoother,info [,ilev ,pos])
call p%set(solver,info [,ilev ,pos])

In this way, the variable will act as a mold to which the preconditioner will conform, even though the MLD2P4 library is not modified, and thus has no direct knowledge about the new type.

Arguments

p	type(mld_xprec_type), intent(inout).
	The preconditioner data structure. Note that x must be chosen according to the real/complex, single/double precision version of MLD2P4 under use.
what	integer, intent(in) or character(len=*).
	The parameter to be set. It can be specified by a predefined constant, or through its name; the string is case-insensitive. See also Tables 2-6.
val	integer or character(len=*) or real(psb_spk_) or real(psb_dpk_), intent(in).
	The value of the parameter to be set. The list of allowed values and the corresponding data types is given in Tables 2-6. When the value is of type character(len=*), it is also treated as case insensitive.
smoother	class(mld_x_base_smoother_type)
	The user-defined new smoother to be employed in the preconditioner.
solver	class(mld_x_base_solver_type)
	The user-defined new solver to be employed in the preconditioner.
info	integer, intent(out).
	Error code. If no error, 0 is returned. See Section 7 for details.
pos	charater(len=*), intent(in).
	Whether the other arguments apply to the 'PRE' or to the 'POST' smoothers.

A variety of (one-level and multi-level) preconditioners can be obtained by a suitable setting of the preconditioner parameters. These parameters can be logically divided into four groups, i.e. parameters defining

1. the type of multi-level preconditioner;
2. the one-level preconditioner used as smoother;
3. the aggregation algorithm;
4. the coarse-space correction at the coarsest level.

A list of the parameters that can be set, along with their allowed and default values, is given in Tables 2-6. For a detailed description of the meaning of the parameters, please refer to Section 4.

The smoother and solver objects are arranged in a hierarchical manner; when specifying a smoother object, its parameters including the contained solver are set to default values, and when a solver object is specified its defaults are also set, overriding in both cases any previous settings even if explicitly specified. Therefore if the user sets a new smoother, and wishes to use a solver different from the default one, the call to set the solver must come after the call to set the smoother.

The combination of a Jacobi smoother with a Diagonal Scaling local solver is equivalent to the strategy called Point Jacobi in the literature; similarly, having a Jacobi smoother with a Gauss-Seidel local solver is equivalent to a ``hybrid Gauss-Seidel'' solver.

Completely new smoother and/or solver class derived from the base objects in the library may be used without recompiling the library itself. Once the new smoother/solver class has been developed, the user can declare a variable of that new type in the application, and pass that variable to the p%set(solver,info) call; the new solver object is then dynamically included in the preconditioner structure.

The what,val pairs described here are those of the predefined smoother/solver objects; newly developed solvers may define new pairs according to their needs.

Table 2: Parameters defining the type of multi-level preconditioner.

what	DATA TYPE	val	DEFAULT	COMMENTS
mld_ml_type_ ML_TYPE	character(len=*)	'ADD' 'MULT'	'MULT'	Basic multi-level framework: additive or multiplicative among the levels (always additive inside a level).
mld_smoother_type_ SMOOTHER_TYPE	character(len=*)	'JACOBI' 'BJAC' 'AS'	'AS'	Basic predefined one-level preconditioner (i.e. smoother): Jacobi, block Jacobi, AS.
mld_smoother_pos_ SMOOTHER_POS	character(len=*)	'PRE' 'POST' 'TWOSIDE'	'TWOSIDE'	``Position'' of the smoother: pre-smoother, post-smoother, pre- and post-smoother.

Table 3: Parameters defining the one-level preconditioner used as smoother.

what	DATA TYPE	val	DEFAULT	COMMENTS
mld_sub_ovr_ SUB_OVR	integer	any int. num. $\ge 0$	1	Number of overlap layers.
mld_sub_restr_ SUB_RESTR	character(len=*)	'HALO' 'NONE'	'HALO'	Type of restriction operator: 'HALO' for taking into account the overlap, 'NONE' for neglecting it.
mld_sub_prol_ SUB_PROL	character(len=*)	'SUM' 'NONE'	'NONE'	Type of prolongation operator: 'SUM' for adding the contributions from the overlap, 'NONE' for neglecting them.
mld_sub_solve_ SUB_SOLVE	character(len=*)	'DIAG' 'GS' 'BWGS' 'ILU' 'MILU' 'ILUT' 'UMF' 'SLU' 'MUMPS'	'ILU'	Predefined local solver: pointwise Jacobi (diagonal scaling), (forward) Gauss-Seidel, Backward Gauss-Seidel, ILU($p$), MILU($p$), ILU($p,t$), LU from UMFPACK, LU from SuperLU (plus triangular solve), LU from MUMPS.
mld_sub_fillin_ SUB_FILLIN	integer	Any int. num. $\ge 0$	0	Fill-in level $p$ of the incomplete LU factorizations.
mld_sub_iluthrs_ SUB_ILUTHRS	real(kind_parameter)	Any real num. $\ge 0$	0	Drop tolerance $t$ in the ILU($p,t$) factorization.
mld_sub_ren_ SUB_REN	character(len=*)	'RENUM_NONE' 'RENUM_GLOBAL'	'RENUM_NONE'	Row and column reordering of the local submatrices: no reordering, reordering according to the global numbering of the rows and columns of the whole matrix.
mld_solver_sweeps_ SOLVER_SWEEPS	integer	Any int. num. $\ge 1$	1	Number of sweeps for iterative local solver (currently only Gauss-Seidel).

Table 4: Parameters defining the aggregation algorithm.

what	DATA TYPE	val	DEFAULT	COMMENTS
mld_coarse_aggr_size_ COARSE_AGGR_SIZE	integer	A positive number	The cubic root of the matrix size at the fine level.	Coarse size threshold. Aggregation will proceed until either the global number of variables is below this threshold, or the aggregation does not reduce the size any longer, or the maximum number of levels is reached.
mld_min_aggr_ratio MIN_AGGR_RATIO	real	A number greater than one	1.5	Minimum aggregation ratio. Aggregation will stop if the ratio between the matrix sizes at two consecutive levels drops below this threshold.
mld_n_prec_levs_ N_PREC_LEVS	integer	A number greater than 1, or -1.	-1	Number of levels; if set to a positive number greater than 1, it will force the aggregation algorithm to use this many levels (unless there is no reduction in the coarse matrix size).
mld_max_prec_levs_ MAX_PREC_LEVS	integer	A positive number	20	Maximum number of levels: irrespective of the other settings, do not use more than this many levels.
mld_aggr_alg_ AGGR_ALG	character(len=*)	'DEC', 'SYMDEC'	'DEC'	Aggregation algorithm. Currently, only the decoupled aggregation is available; the SYMDEC option applies decoupled aggregation to the sparsity pattern of $A+A^T$.
mld_aggr_ord_ AGGR_ORD	character(len=*)	'NATURAL'	'DEGREE'	Initial ordering of indices for aggregation algorithm: natural ordering or sorted by descending degree of the node in the matrix graph. Since aggregation is heuristics, results will be different.

Table 5: Parameters defining the aggregation algorithm.

what	DATA TYPE	val	DEFAULT	COMMENTS
mld_aggr_kind_ AGGR_KIND	character(len=*)	'SMOOTHED' 'NONSMOOTHED'	'SMOOTHED'	Type of aggregation: smoothed, nonsmoothed (i.e. using the tentative prolongator).
mld_aggr_thresh_ AGGR_THRESH	real(kind_parameter)	Any real num. $\in [0, 1]$	0.05	Threshold $\theta$ in the aggregation algorithm.
mld_aggr_scale_ AGGR_SCALE	real(kind_parameter)	Any real num. $\in [0, 1]$	1.0	Scale factor applied to the threshold going from level $ilev$ to level $ilev+1$.
mld_aggr_omega_alg_ AGGR_OMEGA_ALG	character(len=*)	'EIG_EST' 'USER_CHOICE'	'EIG_EST'	How the damping parameter $\omega$ in the smoothed aggregation should be computed: either via an estimate of the spectral radius of $D^{-1}A$, or explicily specified by the user.
mld_aggr_eig_ AGGR_EIG	character(len=*)	'A_NORMI'	'A_NORMI'	How to estimate the spectral radius of $D^{-1}A$. Currently only the infinity norm estimate is available.
mld_aggr_omega_val_ AGGR_OMEGA_VAL	real(kind_parameter)	Any nonnegative real num.	$4/(3\rho(D^{-1}A))$	Damping parameter $\omega$ in the smoothed aggregation algorithm. It must be set by the user if USER_CHOICE was specified for mld_aggr_omega_alg_, otherwise it is computed by the library, using the selected estimate of the spectral radius $\rho(D^{-1}A)$ of $D^{-1}A$.

Table 6: Parameters defining the coarse-space correction at the coarsest level.

what	DATA TYPE	val	DEFAULT	COMMENTS
mld_coarse_mat_ COARSE_MAT	character(len=*)	'DISTR' 'REPL'	'DISTR'	Coarsest matrix: distributed among the processors or replicated on each of them.
mld_coarse_solve_ COARSE_SOLVE	character(len=*)	'BJAC' 'UMF' 'MUMPS' 'SLU' 'SLUDIST'	'BJAC'	Solver used at the coarsest level: block Jacobi, sequential LU from UMFPACK, sequential LU from SuperLU, distributed LU from SuperLU_Dist or MUMPS. 'SLUDIST' and 'MUMPS' require the coarsest matrix to be distributed, while 'UMF' and 'SLU' require it to be replicated.
mld_coarse_subsolve_ COARSE_SUBSOLVE	character(len=*)	'ILU' 'MILU' 'ILUT' 'UMF' 'SLU' 'MUMPS'	See note	Solver for the diagonal blocks of the coarse matrix, in case the block Jacobi solver is chosen as coarsest-level solver: ILU($p$), MILU($p$), ILU($p,t$), LU from UMFPACK, LU from SuperLU, plus triangular solve.
mld_coarse_sweeps_ COARSE_SWEEPS	integer	Any int. num. $> 0$	4	Number of Block-Jacobi sweeps when 'BJAC' is used as coarsest-level solver.
mld_coarse_fillin_ COARSE_FILLIN	integer	Any int. num. $\ge 0$	0	Fill-in level $p$ of the incomplete LU factorizations.
mld_coarse_iluthrs_ COARSE_ILUTHRS	real(kind_parameter)	Any real. num. $\ge 0$	0	Drop tolerance $t$ in the ILU($p,t$) factorization.
Note: defaults for mld_coarse_subsolve_ are chosen as
single precision version: 'MUMPS' if installed, 'SLU' if installed, 'ILU' otherwise
double precision version: 'MUMPS' if installed, 'UMF' if installed, else 'SLU' if installed, 'ILU' otherwise