From: Martin Kronbichler Date: Thu, 7 Feb 2019 17:09:54 +0000 (+0100) Subject: Augment documentation about eigenvalue computation. X-Git-Tag: v9.1.0-rc1~338^2~2 X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=1b3760abe97fe7dbe6efaaccebd356ccc7a35402;p=dealii.git Augment documentation about eigenvalue computation. --- diff --git a/include/deal.II/lac/precondition.h b/include/deal.II/lac/precondition.h index 94ecb0e76f..7d21d2cc0c 100644 --- a/include/deal.II/lac/precondition.h +++ b/include/deal.II/lac/precondition.h @@ -883,7 +883,10 @@ private: * \frac{\lambda_{\max{}}-\lambda_{\min{}}}{\lambda_{\max{}}+\lambda_{\min{}}}$ * for the maximal eigenvalue $\lambda_{\max{}}$ and updated via $\rho_n = * \left(2\frac{\lambda_{\max{}}+\lambda_{\min{}}} - * {\lambda_{\max{}}-\lambda_{\min{}}} - \rho_{n-1}\right)^{-1}$. + * {\lambda_{\max{}}-\lambda_{\min{}}} - \rho_{n-1}\right)^{-1}$. The + * Chebyshev polynomial is constructed to strongly damp the eigenvalue range + * between $\lambda_{\min{}}$ and $\lambda_{\max{}}$ and is visualized e.g. in + * Utilities::LinearAlgebra::chebyshev_filter(). * * The typical use case for the preconditioner is a Jacobi preconditioner * specified through DiagonalMatrix, which is also the default value for the @@ -903,28 +906,52 @@ private: * products are %parallel and the inner preconditioner is %parallel). Its use * is demonstrated in the step-37 tutorial program. * - *

Algorithm execution

+ *

Estimation of the eigenvalues

* * The Chebyshev method relies on an estimate of the eigenvalues of the matrix * which are computed during the first invocation of vmult(). The algorithm * invokes a conjugate gradient solver so symmetry and positive definiteness - * of the (preconditioned) matrix system are strong requirements. As a - * consequence, this class only makes sense if it is applied repeatedly, - * e.g. in a smoother for a multigrid algorithm. The computation of - * eigenvalues needs to be deferred until the first vmult() invocation because + * of the (preconditioned) matrix system are requirements. The eigenvalue + * algorithm can be controlled by + * PreconditionChebyshev::AdditionalData::eig_cg_n_iterations specifying how + * many iterations should be performed. The iterations are started from an + * initial vector that depends on the vector type. For the classes + * dealii::Vector or dealii::LinearAlgebra::distributed::Vector, which have + * fast element access, it is either a vector with entries `(-5.5, -4.5, -3.5, + * -2.5, ..., 3.5, 4.5, 5.5)` with appropriate epilogue and adjusted such that + * its mean is always zero, which works well for the Laplacian. For other + * vector types, the initial vector contains all ones, scaled by the length of + * the vector, except for the very first entry that is zero, triggering + * high-frequency content again. + * + * The computation of eigenvalues happens the first time one of the + * vmult(), Tvmult(), step() or Tstep() functions is called. This is because * temporary vectors of the same layout as the source and destination vectors * are necessary for these computations and this information gets only * available through vmult(). * - * The estimation of eigenvalues can also be bypassed by setting - * PreconditionChebyshev::AdditionalData::eig_cg_n_iterations to zero and - * providing sensible values for the largest eigenvalues in the field - * PreconditionChebyshev::AdditionalData::max_eigenvalue. If the range - * [max_eigenvalue/smoothing_range, max_eigenvalue] contains all - * eigenvalues of the preconditioned matrix system and the degree (i.e., - * number of iterations) is high enough, this class can also be used as a - * direct solver. For an error estimation of the Chebyshev iteration that can - * be used to determine the number of iteration, see Varga (2009). + * Due to the cost of the eigenvalue estimate in the first vmult(), this class + * is most appropriate if it is applied repeatedly, e.g. in a smoother for a + * algorithm. + * + *

Bypassing the eigenvalue computation

+ * + * In some contexts, the automatic eigenvalue computation of this class may + * result in bad quality, or it may be unstable when used in parallel with + * different enumerations of the degrees of freedom, making computations + * strongly dependent on the parallel configuration. It is possible to bypass + * the automatic eigenvalue computation by setting + * AdditionalData::eig_cg_n_iterations to zero, and provide the variable + * AdditionalData::max_eigenvalue instead. The minimal eigenvalue is + * implicitly specified via `max_eigenvalue/smoothing_range`. + + *

Using the PreconditionChebyshev as a solver

+ * + * If the range [max_eigenvalue/smoothing_range, max_eigenvalue] + * contains all eigenvalues of the preconditioned matrix system and the degree + * (i.e., number of iterations) is high enough, this class can also be used as + * a direct solver. For an error estimation of the Chebyshev iteration that + * can be used to determine the number of iteration, see Varga (2009). * * In order to use Chebyshev as a solver, set the degree to * numbers::invalid_unsigned_int to force the automatic computation of the @@ -953,11 +980,11 @@ private: * PreconditionChebyshev. The preconditioner is held in a shared_ptr that is * copied into the AdditionalData member variable of the class, so the * variable used for initialization can safely be discarded after calling - * initialize(). Both the matrix and the preconditioner need to provide @p - * vmult functions for the matrix-vector product and @p m functions for + * initialize(). Both the matrix and the preconditioner need to provide + * @p vmult() functions for the matrix-vector product and @p m() functions for * accessing the number of rows in the (square) matrix. Furthermore, the * matrix must provide el(i,i) methods for accessing the matrix - * diagonal in case the preconditioner type is a diagonal matrix. Even though + * diagonal in case the preconditioner type is DiagonalMatrix. Even though * it is highly recommended to pass the inverse diagonal entries inside a * separate preconditioner object for implementing the Jacobi method (which is * the only possible way to operate this class when computing in %parallel @@ -1040,8 +1067,9 @@ public: /** * Maximum number of CG iterations performed for finding the maximum - * eigenvalue. If set to zero, no computations are performed and the - * eigenvalues according to the given input are used instead. + * eigenvalue. If set to zero, no computations are performed. Instead, the + * user must supply a largest eigenvalue via the variable + * PreconditionChebyshev::AdditionalData::max_eigenvalue. */ unsigned int eig_cg_n_iterations; @@ -2265,6 +2293,11 @@ PreconditionChebyshev:: Vector>::value == false)))) temp_vector2.reinit(src, true); + else + { + VectorType empty_vector; + temp_vector2.reinit(empty_vector); + } const_cast< PreconditionChebyshev *>(this)