Eigenunsupported
3.4.90 (git rev 67eeba6e720c5745abc77ae6c92ce0a44aa7b7ae)

The Induced Dimension Reduction method (IDR(s)) is a shortrecurrences Krylov method for sparse square problems.
This class allows to solve for A.x = b sparse linear problems. The vectors x and b can be either dense or sparse. he Induced Dimension Reduction method, IDR(), is a robust and efficient shortrecurrence Krylov subspace method for solving large nonsymmetric systems of linear equations.
For indefinite systems IDR(S) outperforms both BiCGStab and BiCGStab(L). Additionally, IDR(S) can handle matrices with complex eigenvalues more efficiently than BiCGStab.
Many problems that do not converge for BiCGSTAB converge for IDR(s) (for larger values of s). And if both methods converge the convergence for IDR(s) is typically much faster for difficult systems (for example indefinite problems).
IDR(s) is a limited memory finite termination method. In exact arithmetic it converges in at most N+N/s iterations, with N the system size. It uses a fixed number of 4+3s vector. In comparison, BiCGSTAB terminates in 2N iterations and uses 7 vectors. GMRES terminates in at most N iterations, and uses I+3 vectors, with I the number of iterations. Restarting GMRES limits the memory consumption, but destroys the finite termination property.
MatrixType_  the type of the sparse matrix A, can be a dense or a sparse matrix. 
Preconditioner_  the type of the preconditioner. Default is DiagonalPreconditioner 
This class follows the sparse solver concept .
The maximal number of iterations and tolerance value can be controlled via the setMaxIterations() and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations and NumTraits<Scalar>::epsilon() for the tolerance.
The tolerance corresponds to the relative residual error: Axb/b
Performance: when using sparse matrices, best performance is achied for a rowmajor sparse matrix format. Moreover, in this case multithreading can be exploited if the user code is compiled with OpenMP enabled. See Eigen and multithreading for details.
By default the iterations start with x=0 as an initial guess of the solution. One can control the start using the solveWithGuess() method.
IDR(s) can also be used in a matrixfree context, see the following example .
Public Member Functions  
template<typename Rhs , typename Dest >  
void  _solve_vector_with_guess_impl (const Rhs &b, Dest &x) const 
IDRS ()  
template<typename MatrixDerived >  
IDRS (const EigenBase< MatrixDerived > &A)  
void  setAngle (RealScalar angle) 
void  setResidualUpdate (bool update) 
void  setS (Index S) 
void  setSmoothing (bool smoothing) 

inline 
Default constructor.

inlineexplicit 
Initialize the solver with matrix A for further Ax=b
solving.
This constructor is a shortcut for the default constructor followed by a call to compute().

inline 
Loops over the number of columns of b and does the following:

inline 
The angle must be a real scalar. In IDR(s), a value for the iteration parameter omega must be chosen in every s+1th step. The most natural choice is to select a value to minimize the norm of the next residual. This corresponds to the parameter omega = 0. In practice, this may lead to values of omega that are so small that the other iteration parameters cannot be computed with sufficient accuracy. In such cases it is better to increase the value of omega sufficiently such that a compromise is reached between accurate computations and reduction of the residual norm. The parameter angle =0.7 (”maintaining the convergence strategy”) results in such a compromise.

inline 
The parameter replace is a logical that determines whether a residual replacement strategy is employed to increase the accuracy of the solution.

inline 
Sets the parameter S, indicating the dimension of the shadow space. Default is 4

inline 
Switches off and on smoothing. Residual smoothing results in monotonically decreasing residual norms at the expense of two extra vectors of storage and a few extra vector operations. Although monotonic decrease of the residual norms is a desirable property, the rate of convergence of the unsmoothed process and the smoothed process is basically the same. Default is off