#include <Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h>
template<typename Derived>
class Eigen::IterativeSolverBase< Derived >
Base class for linear iterative solvers.
- See also
- class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
◆ IterativeSolverBase() [1/2]
template<typename Derived>
◆ IterativeSolverBase() [2/2]
template<typename Derived>
template<typename MatrixDerived>
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().
- Warning
- this class stores a reference to the matrix A as well as some precomputed values that depend on it. Therefore, if A is changed this class becomes invalid. Call compute() to update it with the new matrix A, or modify a copy of A.
◆ analyzePattern()
template<typename Derived>
template<typename MatrixDerived>
Initializes the iterative solver for the sparsity pattern of the matrix A for further solving Ax=b problems.
Currently, this function mostly calls analyzePattern on the preconditioner. In the future we might, for instance, implement column reordering for faster matrix vector products.
◆ compute()
template<typename Derived>
template<typename MatrixDerived>
Initializes the iterative solver with the matrix A for further solving Ax=b problems.
Currently, this function mostly initializes/computes the preconditioner. In the future we might, for instance, implement column reordering for faster matrix vector products.
- Warning
- this class stores a reference to the matrix A as well as some precomputed values that depend on it. Therefore, if A is changed this class becomes invalid. Call compute() to update it with the new matrix A, or modify a copy of A.
◆ error()
template<typename Derived>
- Returns
- the tolerance error reached during the last solve. It is a close approximation of the true relative residual error |Ax-b|/|b|, unless the solver documents a different measure: LeastSquaresConjugateGradient reports the normal-equation residual |A'(Ax-b)|/|A'b|, and LSMR reports the normal-equation residual estimate |A'(Ax-b)|/(|A| |Ax-b|).
◆ factorize()
template<typename Derived>
template<typename MatrixDerived>
Initializes the iterative solver with the numerical values of the matrix A for further solving Ax=b problems.
Currently, this function mostly calls factorize on the preconditioner.
- Warning
- this class stores a reference to the matrix A as well as some precomputed values that depend on it. Therefore, if A is changed this class becomes invalid. Call compute() to update it with the new matrix A, or modify a copy of A.
◆ info()
template<typename Derived>
- Returns
- Success if the iterations converged, and NoConvergence otherwise.
◆ iterations()
template<typename Derived>
- Returns
- the number of iterations performed during the last solve
◆ maxIterations()
template<typename Derived>
- Returns
- the max number of iterations. It is either the value set by setMaxIterations or, by default, twice the number of columns of the matrix.
◆ preconditioner() [1/2]
template<typename Derived>
- Returns
- a read-write reference to the preconditioner for custom configuration.
◆ preconditioner() [2/2]
template<typename Derived>
- Returns
- a read-only reference to the preconditioner.
◆ setMaxIterations()
template<typename Derived>
Sets the max number of iterations. Default is twice the number of columns of the matrix.
◆ setTolerance()
template<typename Derived>
Sets the tolerance threshold used by the stopping criteria.
This value is used as an upper bound to the relative residual error: |Ax-b|/|b|, or to the measure the solver documents instead; LeastSquaresConjugateGradient bounds the normal-equation residual |A'(Ax-b)|/|A'b|, and LSMR bounds |A'(Ax-b)|/(|A| |Ax-b|). The default value is the machine precision given by NumTraits<Scalar>::epsilon()
◆ solveWithGuess()
template<typename Derived>
template<typename Rhs, typename Guess>
- Returns
- the solution x of \( A x = b \) using the current decomposition of A and x0 as an initial solution.
- See also
- solve(), compute()
◆ solveWithGuessInPlace()
template<typename Derived>
template<typename Rhs, typename Dest>
Solves \( A x = b \) in place: x holds the initial guess on entry and the solution on exit, and info(), iterations() and error() report on the run.
solve() and solveWithGuess() return expressions and therefore require Eigen dense or sparse operands. This entry point accepts any single-column right-hand side and solution types that provide the vector operations the algorithm uses, for instance the device-resident vectors of the GPU module (contrib/Eigen/GPU) together with a matrix-free matrix type.
- See also
- solve(), solveWithGuess()
◆ tolerance()
template<typename Derived>
- Returns
- the tolerance threshold used by the stopping criteria.
- See also
- setTolerance()
The documentation for this class was generated from the following file:
Inheritance diagram for Eigen::IterativeSolverBase< Derived >:
1.13.2 