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| ConjugateGradient () |
| Default constructor. More...
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template<typename MatrixDerived > |
| ConjugateGradient (const EigenBase< MatrixDerived > &A) |
| Initialize the solver with matrix A for further Ax=b solving. More...
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| ~ConjugateGradient () |
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template<typename Rhs , typename Dest > |
void | _solve_vector_with_guess_impl (const Rhs &b, Dest &x) const |
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| IterativeSolverBase () |
| Default constructor. More...
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| IterativeSolverBase (const EigenBase< MatrixDerived > &A) |
| Initialize the solver with matrix A for further Ax=b solving. More...
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| ~IterativeSolverBase () |
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ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | analyzePattern (const EigenBase< MatrixDerived > &A) |
| Initializes the iterative solver for the sparsity pattern of the matrix A for further solving Ax=b problems. More...
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ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | factorize (const EigenBase< MatrixDerived > &A) |
| Initializes the iterative solver with the numerical values of the matrix A for further solving Ax=b problems. More...
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ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | compute (const EigenBase< MatrixDerived > &A) |
| Initializes the iterative solver with the matrix A for further solving Ax=b problems. More...
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EIGEN_CONSTEXPR Index | rows () const EIGEN_NOEXCEPT |
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EIGEN_CONSTEXPR Index | cols () const EIGEN_NOEXCEPT |
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RealScalar | tolerance () const |
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ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | setTolerance (const RealScalar &tolerance) |
| Sets the tolerance threshold used by the stopping criteria. More...
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Preconditioner & | preconditioner () |
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const Preconditioner & | preconditioner () const |
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Index | maxIterations () const |
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ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | setMaxIterations (Index maxIters) |
| Sets the max number of iterations. More...
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Index | iterations () const |
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RealScalar | error () const |
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const SolveWithGuess< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >, Rhs, Guess > | solveWithGuess (const MatrixBase< Rhs > &b, const Guess &x0) const |
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ComputationInfo | info () const |
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void | _solve_with_guess_impl (const Rhs &b, SparseMatrixBase< DestDerived > &aDest) const |
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internal::enable_if< Rhs::ColsAtCompileTime!=1 &&DestDerived::ColsAtCompileTime!=1 >::type | _solve_with_guess_impl (const Rhs &b, MatrixBase< DestDerived > &aDest) const |
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internal::enable_if< Rhs::ColsAtCompileTime==1||DestDerived::ColsAtCompileTime==1 >::type | _solve_with_guess_impl (const Rhs &b, MatrixBase< DestDerived > &dest) const |
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void | _solve_impl (const Rhs &b, Dest &x) const |
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ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | derived () |
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const ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | derived () const |
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| SparseSolverBase () |
| Default constructor. More...
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| ~SparseSolverBase () |
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ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | derived () |
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const ConjugateGradient< _MatrixType, _UpLo, _Preconditioner > & | derived () const |
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const Solve< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >, Rhs > | solve (const MatrixBase< Rhs > &b) const |
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const Solve< ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >, Rhs > | solve (const SparseMatrixBase< Rhs > &b) const |
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void | _solve_impl (const SparseMatrixBase< Rhs > &b, SparseMatrixBase< Dest > &dest) const |
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template<typename _MatrixType, int _UpLo, typename _Preconditioner>
class Eigen::ConjugateGradient< _MatrixType, _UpLo, _Preconditioner >
A conjugate gradient solver for sparse (or dense) self-adjoint problems.
This class allows to solve for A.x = b linear problems using an iterative conjugate gradient algorithm. The matrix A must be selfadjoint. The matrix A and the vectors x and b can be either dense or sparse.
- Template Parameters
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_MatrixType | the type of the matrix A, can be a dense or a sparse matrix. |
_UpLo | the triangular part that will be used for the computations. It can be Lower, Upper , or Lower|Upper in which the full matrix entries will be considered. Default is Lower , best performance is Lower|Upper . |
_Preconditioner | the type of the preconditioner. Default is DiagonalPreconditioner |
\implsparsesolverconcept
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: |Ax-b|/|b|
Performance: Even though the default value of _UpLo
is Lower
, significantly higher performance is achieved when using a complete matrix and Lower|Upper as the _UpLo template parameter. Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled. See TopicMultiThreading for details.
This class can be used as the direct solver classes. Here is a typical usage example:
int n = 10000;
cg.compute(A);
std::cout << "#iterations: " << cg.iterations() << std::endl;
std::cout << "estimated error: " << cg.error() << std::endl;
A conjugate gradient solver for sparse (or dense) self-adjoint problems.
Definition: ConjugateGradient.h:159
A versatible sparse matrix representation.
Definition: SparseMatrix.h:98
@ Lower
View matrix as a lower triangular matrix.
Definition: Constants.h:209
@ Upper
View matrix as an upper triangular matrix.
Definition: Constants.h:211
By default the iterations start with x=0 as an initial guess of the solution. One can control the start using the solveWithGuess() method.
ConjugateGradient can also be used in a matrix-free context, see the following example .
- See also
- class LeastSquaresConjugateGradient, class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner