Linear solvers

The purpose of this tutorial is to show how to specify different linear solvers for `oomph-lib's`

Newton solver.

- Overview
- List of available linear solvers
- How to change the LinearSolver
- Problem-specific preconditioners

As discussed in the bottom-up discussion of `oomph-lib's`

data structure, by default `oomph-lib's`

Newton solver, `Problem::newton_solve`

(...) solves the linear systems arising during the Newton iteration with its default linear solver, ` SuperLUSolver`

, a wrapper to Demmel, Eistenstat, Gilbert, Li & Liu's sparse direct solver SuperLU.

`oomph-lib`

provides a large number of alternative linear solvers that may be used instead. All linear solvers within the library are derived from the base class ` LinearSolver`

which contains a single pure virtual function

virtual void solve(Problem* const &problem_pt, DoubleVector &result)=0;

whose task it is to compute the solution (returned in the vector `result`

) of the linear system

where and are the global Jacobian and the residual vector, computed by the `Problem`

pointed to by `problem_pt`

. The ` LinearSolver`

class also defines linear-algebra-type interfaces that allow the solution of linear systems with matrices other than the `Problem's`

Jacobian matrix. However, these methods may not be implemented for all linear solvers.

`oomph-lib's`

linear solvers can be sub-divided into serial and parallel, and direct and iterative linear solvers. Here is a quick overview of the available linear solvers. If you are viewing this document online, the links take you directly to the solvers' class references which explain any solver-specific member functions.

**Serial solvers:**

**Direct solvers:**

`SuperLUSolver`

:`oomph-lib's`

default linear solver, a wrapper to Demmel, Eistenstat, Gilbert, Li & Liu's sparse direct solver SuperLU.

`HSL_MA42`

: A wrapper to the frontal solver MA42 from the HSL library. This solver is available free-of-charge for UK academics. The source code can be dropped into the`oomph-lib`

distribution; see the instructions in the dummy code

external_src/oomph_hsl/dummy_frontal.f

`DenseLU`

: A direct solver, based on the LU decomposition of the Jacobian matrix which is stored as a dense matrix. Given that the Jacobian matrices arising from most problems are sparse, this is likely to be a very inefficient solver. It is mainly used by the derived (and even more inefficient!) solver`FD_LU`

.

`FD_LU`

: Almost certainly the world's most inefficient solver. It computes the Jacobian matrix by finite differencing the global residual vector, without taking any sparsity into account. Mainly used by developers as a last-resort sanity check.

**Iterative solvers:**

`oomph-lib`

provides its own implementations of various standard iterative linear solvers. They are derived from the base class`IterativeLinearSolver`

and are typically templated by the matrix type used store the Jacobian matrix. In most cases you will want to set the template argument`MATRIX`

to`CRDoubleMatrix:`

`GMRES`

: A Krylov subspace solver for symmetric and non-symmetric linear systems. The memory usage increases with each iteration but the iteration can be restarted.

`BiCGStab`

: A Krylov subspace method for symmetric and non-symmetric linear systems. The memory requirement remains constant throughout the iteration.

`CG`

: The classical conjugate gradient method for symmetric positive definite matrices. The memory requirement remains constant throughout the iteration.

`GS`

: Gauss-Seidel – a stationary iterative solver.

`oomph-lib`

also provides wrappers to third-party iterative linear solvers. These tend to provide their own implementations of`GMRES`

,`BiCGStab`

,`CG`

, etc. but are not necessarily derived from`oomph-lib's`

own`IterativeLinearSolver`

base class.

`HypreSolver`

: A wrapper to the high-performance linear solvers/preconditioners from the Scalable Linear Solvers Project.

`TrilinosAztecOOSolver`

: A wrapper to the linear solvers from the Trilinos Project.

**Parallel solvers:**

**Direct solvers:**

- When
`oomph-lib`

is compiled with MPI support, its default linear solver`SuperLUSolver`

becomes a wrapper to Demmel, Eistenstat, Gilbert, Li & Liu's parallel sparse direct solver SuperLU_DIST. This behaviour can be over-ruled with the member functionwhose argument must specify one of the three options listed in the enumerationSuperLUSolver::set_solver_type(...)`SuperLUSolver::Type`

. This allows the serial solver SuperLU to be used even if`oomph-lib`

is compiled with MPI support.

`MumpsSolver`

: is a wrapper to the MUMPS multifrontal solver that is available when`oomph-lib`

is compiled with MPI support and support for MUMPS.

- When
**Iterative solvers:**

`HypreSolver`

: A wrapper to the high-performance linear solvers/preconditioners from the Scalable Linear Solvers Project.

`TrilinosAztecOOSolver`

: A wrapper to the linear solvers from the Trilinos Project.

Changing `oomph-lib's`

linear solver is straightforward. For instance, to change the linear solver to `oomph-lib's`

`DenseLU`

solver, simply create an instance of this solver and pass a pointer to it to the `Problem`

. This most easily done in the `Problem`

constructor:

// Change solver to DenseLU

linear_solver_pt()=new DenseLU;

In any subsequent calls to `oomph-lib's`

Newton solver, `DenseLU`

will now be used to solve the linear systems arising during the Newton iteration.

The specification of an iterative linear solver is just as easy: For instance, to specify `oomph-lib's`

conjugate gradient solver `CG`

(storing the Jacobian matrix in compressed row format) as the linear solver, add

// Change solver to CG

IterativeLinearSolver* solver_pt=new CG<CRDoubleMatrix>;

linear_solver_pt()=solver_pt;

to the problem constructor. We note that, by default, `oomph-lib's`

`IterativeLinearSolvers`

perform the preconditioning using the trivial "identity preconditioner". Most Krylov subspace solvers perform very poorly without some sort of preconditioning.

Specific preconditioners may be implemented by deriving from the `Preconditioner`

base class, by implementing its two pure virtual functions

/// \short Apply the preconditioner. Pure virtual generic interface

/// function. This method should apply the preconditioner operator to the

/// vector r and return the vector z.

virtual void preconditioner_solve(const DoubleVector &r, DoubleVector &z)

= 0;

and

/// \short Setup the preconditioner. Pure virtual generic interface

/// function.

virtual void setup() = 0;

Note that, by default, `oomph-lib's`

`IterativeLinearSolvers`

employ left preconditioning.

`oomph-lib`

provides fully-functional implementations of several general-purpose preconditioners. For instance, the zero-fill-in incomplete LU factorisation preconditioner ILU(0) may be employed by adding the lines

// Specify preconditioner

solver_pt->preconditioner_pt()=new ILUZeroPreconditioner<CRDoubleMatrix>;

to the `Problem`

constructor.

Of particular interest is the availability of an "exact preconditioner" `SuperLUPreconditioner`

whose use guarantees the convergence of any iterative solver within a single iteration – useful for code development.

`oomph-lib`

provides wrappers to various third-party iterative linear solvers and preconditioners. We stress that these solvers are not necessarily implemented as `oomph-lib`

`IterativeLinearSolvers`

since their interfaces for the specification of preconditioners, etc may differ from those employed by `oomph-lib`

. Furthermore, unlike `SuperLUSolver`

these solvers are not distributed as part of `oomph-lib`

so you have to build/install them separately before installing `oomph-lib`

(`oomph-lib's`

build machinery can do this for you if you wish; see installation instructions for details). Once this is done, they may be used like any other linear solver.

`oomph-lib`

provides wrappers to the iterative linear solvers/preconditioners from the Trilinos Project. The demo code TrilinosSolver_test.cc demonstrates how use various combinations of solvers/preconditioners. Here is a brief overview:

**Trilinos solvers**

The wrappers to Trilinos' Krylov subspace solvers are implemented as `oomph-lib`

`IterativeLinearSolvers`

, allowing them to be used via the standard interfaces described above. For instance, to use `oomph-lib's`

wrapper to `Trilinos'`

Aztec solver, using `Trilinos'`

`ML`

multilevel preconditioner, set the solvers and preconditioners as usual:

// Create a Trilinos Solver

TrilinosAztecOOSolver* linear_solver_pt = new TrilinosAztecOOSolver;

// Create the Trilinos ML preconditioner

TrilinosMLPreconditioner* preconditioner_pt = new TrilinosMLPreconditioner;

// Set the preconditioner pointer

linear_solver_pt->preconditioner_pt() = preconditioner_pt;

// Set linear solver

problem.linear_solver_pt() = linear_solver_pt;

The actual Krylov subspace solver used by the `Trilinos`

solver is specified by passing an enumerated flag (defined as static member data in the `TrilinosAztecOOSolver`

class) to the solver. For instance, `Trilinos'`

CG, GMRES and BiCGStab solvers are selected with

linear_solver_pt->solver_type() = TrilinosAztecOOSolver::CG;

or

linear_solver_pt->solver_type() = TrilinosAztecOOSolver::GMRES;

or

linear_solver_pt->solver_type() = TrilinosAztecOOSolver::BiCGStab;

respectively.

**Trilinos preconditioners**

`oomph-lib`

provides wrappers to `Trilinos'`

ML and IFPACK preconditioners that allows them to be used as `oomph-lib`

`Preconditioners`

that may be used with `oomph-lib's`

own `IterativeLinearSolvers`

. Here is an example that shows how to build an instance of `oomph-lib's`

`GMRES`

, preconditioned with its wrapper to `Trilinos'`

`IFPACK`

preconditioner:

// Create oomph-lib linear solver

IterativeLinearSolver* linear_solver_pt=new GMRES<CRDoubleMatrix>;

// Create Trilinos IFPACK preconditioner as oomph-lib Preconditioner

Preconditioner* preconditioner_pt=new TrilinosIFPACKPreconditioner;

// Pass pointer to preconditioner to oomph-lib IterativeLinearSolver

linear_solver_pt->preconditioner_pt()=preconditioner_pt;

`oomph-lib`

provides wrappers to the high-performance linear solvers/preconditioners from the Scalable Linear Solvers Project. The demo code HypreSolver_test.cc demonstrates how use various combinations of solvers/preconditioners. Here is a brief overview:

**Hypre solvers**

The wrappers to Hypre's Krylov subspace and AMG solvers are implemented as `oomph-lib`

`LinearSolvers`

(not `IterativeLinearSolvers!`

) so the interfaces for the specification of preconditioners etc. differ from those for `oomph-lib's`

own `IterativeLinearSolvers`

.

The `HypreSolver`

is set like any other `LinearSolver:`

// Create a new Hypre linear solver

HypreSolver* hypre_linear_solver_pt = new HypreSolver;

// Set the linear solver for problem

problem.linear_solver_pt() = hypre_linear_solver_pt;

The actual solver used by the `HypreSolver`

is specified by passing an enumerated flag (defined as static member data in the `HypreSolver`

class) to the solver. For instance, `Hypre's`

AMG, CG, GMRES and BiCGStab solvers are selected with

hypre_linear_solver_pt->hypre_method() = HypreSolver::BoomerAMG;

or

hypre_linear_solver_pt->hypre_method() = HypreSolver::CG;

or

hypre_linear_solver_pt->hypre_method() = HypreSolver::GMRES;

or

hypre_linear_solver_pt->hypre_method() = HypreSolver::BiCGStab;

respectively.

These Krylov subspace methods may then be preconditioned by `Hypre's`

own (internal) preconditioners, again by specifying the method via an enumerated flag. So, to use no preconditioning, or to precondition with `BoomerAMG`

(and AMG-based preconditioner), `Euclid`

(an ILU preconditioner) or `ParaSails`

(a sparse approximate inverse preconditioner), set:

hypre_linear_solver_pt->internal_preconditioner()=HypreSolver::None;

or

hypre_linear_solver_pt->internal_preconditioner()=HypreSolver::BoomerAMG;

or

hypre_linear_solver_pt->internal_preconditioner()=HypreSolver::Euclid;

hypre_linear_solver_pt->internal_preconditioner()=HypreSolver::ParaSails;

respectively.

**Hypre preconditioners**

`oomph-lib`

provides wrappers to `Hypre's`

preconditioners that allows them to be used as `oomph-lib`

`Preconditioners`

that may be used with `oomph-lib's`

own `IterativeLinearSolvers`

. Here is an example that shows how to build an instance of `oomph-lib's`

`BiCGStab`

and to use the `HyprePreconditioner`

as the preconditioner:

// Build and instance of BiCGStab and pass it to the problem

oomph_linear_solver_pt = new BiCGStab<CRDoubleMatrix>;

problem.linear_solver_pt() = oomph_linear_solver_pt;

Now we build an instance of a `HyprePreconditioner`

// Create a new Hypre preconditioner

HyprePreconditioner* hypre_preconditioner_pt = new HyprePreconditioner;

and set it as the `Preconditioner`

for `oomph-lib's`

`BiCGStab`

solver:

oomph_linear_solver_pt->preconditioner_pt()=hypre_preconditioner_pt;

The actual preconditioning methodology to be used by the `HyprePreconditioner`

is again selected via enumerated flags, i.e.

hypre_preconditioner_pt->hypre_method() = HyprePreconditioner::BoomerAMG;

or

hypre_preconditioner_pt->hypre_method() = HyprePreconditioner::Euclid;

or

hypre_preconditioner_pt->hypre_method() = HyprePreconditioner::ParaSails;

In addition to "general-purpose" preconditioners like ILU, `oomph-lib`

provides a number of problem-specific preconditioners which are typically based on the library's block preconditioning framework. Separate documentation is available for these:

- We provide a (very!) detailed discussion of
`oomph-lib's`

block preconditioning framework.

- Another tutorial discusses
`oomph-lib's`

"general purpose" block preconditioners.

- The NavierStokesSchurComplementPreconditioner for Navier-Stokes problems is described in its own tutorial.

- The FSIPreconditioner for monolithically-discretised fluid-structure interaction problems is described in its own tutorial

- We provide a preconditioner for large-displacement solid mechanics problems in which boundary displacements are prescribed.

- The previous preconditioner is mainly used as a subsidiary block preconditioner for the solution of fluid-structure interaction problems with (pseudo-)solid fluid mesh updates.

A pdf version of this document is available.