In Eigen, there are several methods available to solve linear systems when the coefficient matrix is sparse. Because of the special representation of this class of matrices, special care should be taken in order to get a good performance. See Sparse matrix manipulations for a detailed introduction about sparse matrices in Eigen. This page lists the sparse solvers available in Eigen. The main steps that are common to all these linear solvers are introduced as well. Depending on the properties of the matrix, the desired accuracy, the end-user is able to tune those steps in order to improve the performance of its code. Note that it is not required to know deeply what's hiding behind these steps: the last section presents a benchmark routine that can be easily used to get an insight on the performance of all the available solvers.

List of sparse solvers

Eigen currently provides a wide set of built-in solvers, as well as wrappers to external solver libraries. They are summarized in the following tables:

Built-in direct solvers

Class	Solver kind	Matrix kind	Features related to performance	License	Notes
SimplicialLLT #include<Eigen/SparseCholesky>	Direct LLt factorization	SPD	Fill-in reducing	LGPL	SimplicialLDLT is often preferable
SimplicialLDLT #include<Eigen/SparseCholesky>	Direct LDLt factorization	SPD	Fill-in reducing	LGPL	Recommended for very sparse and not too large problems (e.g., 2D Poisson eq.)
SparseLU #include<Eigen/SparseLU>	LU factorization	Square	Fill-in reducing, Leverage fast dense algebra	MPL2	optimized for small and large problems with irregular patterns
SparseQR #include<Eigen/SparseQR>	QR factorization	Any, rectangular	Fill-in reducing	MPL2	recommended for least-square problems, has a basic rank-revealing feature

Built-in iterative solvers

Class	Solver kind	Matrix kind	Supported preconditioners, [default]	License	Notes
ConjugateGradient #include<Eigen/IterativeLinearSolvers>	Classic iterative CG	SPD	IdentityPreconditioner, [DiagonalPreconditioner], IncompleteCholesky	MPL2	Recommended for large symmetric problems (e.g., 3D Poisson eq.)
LeastSquaresConjugateGradient #include<Eigen/IterativeLinearSolvers>	CG for rectangular least-square problem	Rectangular	IdentityPreconditioner, [LeastSquareDiagonalPreconditioner]	MPL2	Solve for min |A'Ax-b|^2 without forming A'A
BiCGSTAB #include<Eigen/IterativeLinearSolvers>	Iterative stabilized bi-conjugate gradient	Square	IdentityPreconditioner, [DiagonalPreconditioner], IncompleteLUT	MPL2	To speedup the convergence, try it with the IncompleteLUT preconditioner.

Wrappers to external solvers

Class	Module	Solver kind	Matrix kind	Features related to performance	Dependencies,License	Notes
PastixLLT PastixLDLT PastixLU	PaStiXSupport	Direct LLt, LDLt, LU factorizations	SPD SPD Square	Fill-in reducing, Leverage fast dense algebra, Multithreading	Requires the PaStiX package, CeCILL-C	optimized for tough problems and symmetric patterns
CholmodSupernodalLLT	CholmodSupport	Direct LLt factorization	SPD	Fill-in reducing, Leverage fast dense algebra	Requires the SuiteSparse package, GPL
UmfPackLU	UmfPackSupport	Direct LU factorization	Square	Fill-in reducing, Leverage fast dense algebra	Requires the SuiteSparse package, GPL
KLU	KLUSupport	Direct LU factorization	Square	Fill-in reducing, suitted for circuit simulation	Requires the SuiteSparse package, GPL
SuperLU	SuperLUSupport	Direct LU factorization	Square	Fill-in reducing, Leverage fast dense algebra	Requires the SuperLU library, (BSD-like)
SPQR	SPQRSupport	QR factorization	Any, rectangular	fill-in reducing, multithreaded, fast dense algebra	requires the SuiteSparse package, GPL	recommended for linear least-squares problems, has a rank-revealing feature
PardisoLLT PardisoLDLT PardisoLU	PardisoSupport	Direct LLt, LDLt, LU factorizations	SPD SPD Square	Fill-in reducing, Leverage fast dense algebra, Multithreading	Requires the Intel MKL package, Proprietary	optimized for tough problems patterns, see also using MKL with Eigen

Here SPD means symmetric positive definite.

Sparse solver concept

All these solvers follow the same general concept. Here is a typical and general example:

#include <Eigen/RequiredModuleName>
// ...
SparseMatrix<double> A;
// fill A
VectorXd b, x;
// fill b
// solve Ax = b
SolverClassName<SparseMatrix<double> > solver;
solver.compute(A);
if(solver.info()!=Success) {
  // decomposition failed
  return;
}
x = solver.solve(b);
if(solver.info()!=Success) {
  // solving failed
  return;
}
// solve for another right hand side:
x1 = solver.solve(b1);

For SPD solvers, a second optional template argument allows to specify which triangular part have to be used, e.g.:

#include <Eigen/IterativeLinearSolvers>
 
ConjugateGradient<SparseMatrix<double>, Eigen::Upper> solver;
x = solver.compute(A).solve(b);

In the above example, only the upper triangular part of the input matrix A is considered for solving. The opposite triangle might either be empty or contain arbitrary values.

In the case where multiple problems with the same sparsity pattern have to be solved, then the "compute" step can be decomposed as follow:

SolverClassName<SparseMatrix<double> > solver;
solver.analyzePattern(A);   // for this step the numerical values of A are not used
solver.factorize(A);
x1 = solver.solve(b1);
x2 = solver.solve(b2);
...
A = ...;                    // modify the values of the nonzeros of A, the nonzeros pattern must stay unchanged
solver.factorize(A);
x1 = solver.solve(b1);
x2 = solver.solve(b2);
...

The compute() method is equivalent to calling both analyzePattern() and factorize().

Each solver provides some specific features, such as determinant, access to the factors, controls of the iterations, and so on. More details are available in the documentations of the respective classes.

Finally, most of the iterative solvers, can also be used in a matrix-free context, see the following example .

The Compute Step

In the compute() function, the matrix is generally factorized: LLT for self-adjoint matrices, LDLT for general hermitian matrices, LU for non hermitian matrices and QR for rectangular matrices. These are the results of using direct solvers. For this class of solvers precisely, the compute step is further subdivided into analyzePattern() and factorize().

The goal of analyzePattern() is to reorder the nonzero elements of the matrix, such that the factorization step creates less fill-in. This step exploits only the structure of the matrix. Hence, the results of this step can be used for other linear systems where the matrix has the same structure. Note however that sometimes, some external solvers (like SuperLU) require that the values of the matrix are set in this step, for instance to equilibrate the rows and columns of the matrix. In this situation, the results of this step should not be used with other matrices.

Eigen provides a limited set of methods to reorder the matrix in this step, either built-in (COLAMD, AMD) or external (METIS). These methods are set in template parameter list of the solver :

DirectSolverClassName<SparseMatrix<double>, OrderingMethod<IndexType> > solver;

See the OrderingMethods module for the list of available methods and the associated options.

In factorize(), the factors of the coefficient matrix are computed. This step should be called each time the values of the matrix change. However, the structural pattern of the matrix should not change between multiple calls.

For iterative solvers, the compute step is used to eventually setup a preconditioner. For instance, with the ILUT preconditioner, the incomplete factors L and U are computed in this step. Remember that, basically, the goal of the preconditioner is to speedup the convergence of an iterative method by solving a modified linear system where the coefficient matrix has more clustered eigenvalues. For real problems, an iterative solver should always be used with a preconditioner. In Eigen, a preconditioner is selected by simply adding it as a template parameter to the iterative solver object.

IterativeSolverClassName<SparseMatrix<double>, PreconditionerName<SparseMatrix<double> > solver; 

The member function preconditioner() returns a read-write reference to the preconditioner to directly interact with it. See the Iterative solvers module and the documentation of each class for the list of available methods.

The Solve step

The solve() function computes the solution of the linear systems with one or many right hand sides.

X = solver.solve(B);

Here, B can be a vector or a matrix where the columns form the different right hand sides. The solve() function can be called several times as well, for instance when all the right hand sides are not available at once.

x1 = solver.solve(b1);
// Get the second right hand side b2
x2 = solver.solve(b2); 
//  ...

For direct methods, the solution are computed at the machine precision. Sometimes, the solution need not be too accurate. In this case, the iterative methods are more suitable and the desired accuracy can be set before the solve step using setTolerance(). For all the available functions, please, refer to the documentation of the Iterative solvers module .

BenchmarkRoutine

Most of the time, all you need is to know how much time it will take to solve your system, and hopefully, what is the most suitable solver. In Eigen, we provide a benchmark routine that can be used for this purpose. It is very easy to use. In the build directory, navigate to bench/spbench and compile the routine by typing make spbenchsolver. Run it with –help option to get the list of all available options. Basically, the matrices to test should be in MatrixMarket Coordinate format, and the routine returns the statistics from all available solvers in Eigen.

To export your matrices and right-hand-side vectors in the matrix-market format, you can the the unsupported SparseExtra module:

#include <unsupported/Eigen/SparseExtra>
...
Eigen::saveMarket(A, "filename.mtx");
Eigen::saveMarket(A, "filename_SPD.mtx", Eigen::Symmetric); // if A is symmetric-positive-definite
Eigen::saveMarketVector(B, "filename_b.mtx");

The following table gives an example of XML statistics from several Eigen built-in and external solvers.

Matrix	N	NNZ		UMFPACK	SUPERLU	PASTIX LU	BiCGSTAB	BiCGSTAB+ILUT	GMRES+ILUT	LDLT	CHOLMOD LDLT	PASTIX LDLT	LLT	CHOLMOD SP LLT	CHOLMOD LLT	PASTIX LLT	CG
vector_graphics	12855	72069	Compute Time	0.0254549	0.0215677	0.0701827	0.000153388	0.0140107	0.0153709	0.0101601	0.00930502	0.0649689
			Solve Time	0.00337835	0.000951826	0.00484373	0.0374886	0.0046445	0.00847754	0.000541813	0.000293696	0.00485376
			Total Time	0.0288333	0.0225195	0.0750265	0.037642	0.0186552	0.0238484	0.0107019	0.00959871	0.0698227
			Error(Iter)	1.299e-16	2.04207e-16	4.83393e-15	3.94856e-11 (80)	1.03861e-12 (3)	5.81088e-14 (6)	1.97578e-16	1.83927e-16	4.24115e-15
poisson_SPD	19788	308232	Compute Time	0.425026	1.82378	0.617367	0.000478921	1.34001	1.33471	0.796419	0.857573	0.473007	0.814826	0.184719	0.861555	0.470559	0.000458188
			Solve Time	0.0280053	0.0194402	0.0268747	0.249437	0.0548444	0.0926991	0.00850204	0.0053171	0.0258932	0.00874603	0.00578155	0.00530361	0.0248942	0.239093
			Total Time	0.453031	1.84322	0.644241	0.249916	1.39486	1.42741	0.804921	0.862891	0.4989	0.823572	0.190501	0.866859	0.495453	0.239551
			Error(Iter)	4.67146e-16	1.068e-15	1.3397e-15	6.29233e-11 (201)	3.68527e-11 (6)	3.3168e-15 (16)	1.86376e-15	1.31518e-16	1.42593e-15	3.45361e-15	3.14575e-16	2.21723e-15	7.21058e-16	9.06435e-12 (261)
sherman2	1080	23094	Compute Time	0.00631754	0.015052	0.0247514	-	0.0214425	0.0217988
			Solve Time	0.000478424	0.000337998	0.0010291	-	0.00243152	0.00246152
			Total Time	0.00679597	0.01539	0.0257805	-	0.023874	0.0242603
			Error(Iter)	1.83099e-15	8.19351e-15	2.625e-14	1.3678e+69 (1080)	4.1911e-12 (7)	5.0299e-13 (12)
bcsstk01_SPD	48	400	Compute Time	0.000169079	0.00010789	0.000572538	1.425e-06	9.1612e-05	8.3985e-05	5.6489e-05	7.0913e-05	0.000468251	5.7389e-05	8.0212e-05	5.8394e-05	0.000463017	1.333e-06
			Solve Time	1.2288e-05	1.1124e-05	0.000286387	8.5896e-05	1.6381e-05	1.6984e-05	3.095e-06	4.115e-06	0.000325438	3.504e-06	7.369e-06	3.454e-06	0.000294095	6.0516e-05
			Total Time	0.000181367	0.000119014	0.000858925	8.7321e-05	0.000107993	0.000100969	5.9584e-05	7.5028e-05	0.000793689	6.0893e-05	8.7581e-05	6.1848e-05	0.000757112	6.1849e-05
			Error(Iter)	1.03474e-16	2.23046e-16	2.01273e-16	4.87455e-07 (48)	1.03553e-16 (2)	3.55965e-16 (2)	2.48189e-16	1.88808e-16	1.97976e-16	2.37248e-16	1.82701e-16	2.71474e-16	2.11322e-16	3.547e-09 (48)
sherman1	1000	3750	Compute Time	0.00228805	0.00209231	0.00528268	9.846e-06	0.00163522	0.00162155	0.000789259	0.000804495	0.00438269
			Solve Time	0.000213788	9.7983e-05	0.000938831	0.00629835	0.000361764	0.00078794	4.3989e-05	2.5331e-05	0.000917166
			Total Time	0.00250184	0.00219029	0.00622151	0.0063082	0.00199698	0.00240949	0.000833248	0.000829826	0.00529986
			Error(Iter)	1.16839e-16	2.25968e-16	2.59116e-16	3.76779e-11 (248)	4.13343e-11 (4)	2.22347e-14 (10)	2.05861e-16	1.83555e-16	1.02917e-15
young1c	841	4089	Compute Time	0.00235843	0.00217228	0.00568075	1.2735e-05	0.00264866	0.00258236
			Solve Time	0.000329599	0.000168634	0.00080118	0.0534738	0.00187193	0.00450211
			Total Time	0.00268803	0.00234091	0.00648193	0.0534865	0.00452059	0.00708447
			Error(Iter)	1.27029e-16	2.81321e-16	5.0492e-15	8.0507e-11 (706)	3.00447e-12 (8)	1.46532e-12 (16)
mhd1280b	1280	22778	Compute Time	0.00234898	0.00207079	0.00570918	2.5976e-05	0.00302563	0.00298036	0.00144525	0.000919922	0.00426444
			Solve Time	0.00103392	0.000211911	0.00105	0.0110432	0.000628287	0.00392089	0.000138303	6.2446e-05	0.00097564
			Total Time	0.0033829	0.0022827	0.00675918	0.0110692	0.00365392	0.00690124	0.00158355	0.000982368	0.00524008
			Error(Iter)	1.32953e-16	3.08646e-16	6.734e-16	8.83132e-11 (40)	1.51153e-16 (1)	6.08556e-16 (8)	1.89264e-16	1.97477e-16	6.68126e-09
crashbasis	160000	1750416	Compute Time	3.2019	5.7892	15.7573	0.00383515	3.1006	3.09921
			Solve Time	0.261915	0.106225	0.402141	1.49089	0.24888	0.443673
			Total Time	3.46381	5.89542	16.1594	1.49473	3.34948	3.54288
			Error(Iter)	1.76348e-16	4.58395e-16	1.67982e-14	8.64144e-11 (61)	8.5996e-12 (2)	6.04042e-14 (5)