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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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| ConjugateGradient () |
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template<typename MatrixDerived > |
| ConjugateGradient (const EigenBase< MatrixDerived > &A) |
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| ~ConjugateGradient () |
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void | _solve_impl (const Rhs &b, Dest &x) const |
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std::enable_if_t< Rhs::ColsAtCompileTime!=1 &&DestDerived::ColsAtCompileTime!=1 > | _solve_with_guess_impl (const Rhs &b, MatrixBase< DestDerived > &aDest) const |
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std::enable_if_t< Rhs::ColsAtCompileTime==1||DestDerived::ColsAtCompileTime==1 > | _solve_with_guess_impl (const Rhs &b, MatrixBase< DestDerived > &dest) const |
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void | _solve_with_guess_impl (const Rhs &b, SparseMatrixBase< DestDerived > &aDest) const |
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ConjugateGradient< MatrixType_, UpLo_, Preconditioner_ > & | analyzePattern (const EigenBase< MatrixDerived > &A) |
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EIGEN_CONSTEXPR Index | cols () const EIGEN_NOEXCEPT |
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ConjugateGradient< MatrixType_, UpLo_, Preconditioner_ > & | compute (const EigenBase< MatrixDerived > &A) |
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ConjugateGradient< MatrixType_, UpLo_, Preconditioner_ > & | derived () |
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const ConjugateGradient< MatrixType_, UpLo_, Preconditioner_ > & | derived () const |
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RealScalar | error () const |
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ConjugateGradient< MatrixType_, UpLo_, Preconditioner_ > & | factorize (const EigenBase< MatrixDerived > &A) |
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ComputationInfo | info () const |
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Index | iterations () const |
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| IterativeSolverBase () |
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| IterativeSolverBase (const EigenBase< MatrixDerived > &A) |
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| IterativeSolverBase (IterativeSolverBase &&)=default |
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Index | maxIterations () const |
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Preconditioner & | preconditioner () |
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const Preconditioner & | preconditioner () const |
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EIGEN_CONSTEXPR Index | rows () const EIGEN_NOEXCEPT |
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ConjugateGradient< MatrixType_, UpLo_, Preconditioner_ > & | setMaxIterations (Index maxIters) |
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ConjugateGradient< MatrixType_, UpLo_, Preconditioner_ > & | setTolerance (const RealScalar &tolerance) |
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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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RealScalar | tolerance () const |
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| ~IterativeSolverBase () |
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Derived & | derived () |
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const Derived & | derived () const |
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template<typename Rhs > |
const Solve< Derived, Rhs > | solve (const MatrixBase< Rhs > &b) const |
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template<typename Rhs > |
const Solve< Derived, Rhs > | solve (const SparseMatrixBase< Rhs > &b) const |
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| SparseSolverBase () |
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| SparseSolverBase (SparseSolverBase &&other) |
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| ~SparseSolverBase () |
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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
-
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 |
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: |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 Eigen and multi-threading for details.
This class can be used as the direct solver classes. Here is a typical usage example:
SparseMatrix<double>
A(
n,
n);
ConjugateGradient<SparseMatrix<double>,
Lower|
Upper> cg;
std::cout << "#iterations: " << cg.iterations() << std::endl;
std::cout << "estimated error: " << cg.error() << std::endl;
Matrix< double, Dynamic, 1 > VectorXd
DynamicĂ—1 vector of type double.
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
Definition at line 158 of file ConjugateGradient.h.