diff options
Diffstat (limited to 'Eigen')
-rw-r--r-- | Eigen/src/IterativeLinearSolvers/BiCGSTAB.h | 25 |
1 files changed, 2 insertions, 23 deletions
diff --git a/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h index 6fc6ab852..7a46b51fa 100644 --- a/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h +++ b/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h @@ -136,34 +136,13 @@ struct traits<BiCGSTAB<_MatrixType,_Preconditioner> > * and NumTraits<Scalar>::epsilon() for the tolerance. * * This class can be used as the direct solver classes. Here is a typical usage example: - * \code - * int n = 10000; - * VectorXd x(n), b(n); - * SparseMatrix<double> A(n,n); - * // fill A and b - * BiCGSTAB<SparseMatrix<double> > solver; - * solver(A); - * x = solver.solve(b); - * std::cout << "#iterations: " << solver.iterations() << std::endl; - * std::cout << "estimated error: " << solver.error() << std::endl; - * // update b, and solve again - * x = solver.solve(b); - * \endcode + * \include BiCGSTAB_simple.cpp * * By default the iterations start with x=0 as an initial guess of the solution. * One can control the start using the solveWithGuess() method. Here is a step by * step execution example starting with a random guess and printing the evolution * of the estimated error: - * * \code - * x = VectorXd::Random(n); - * solver.setMaxIterations(1); - * int i = 0; - * do { - * x = solver.solveWithGuess(b,x); - * std::cout << i << " : " << solver.error() << std::endl; - * ++i; - * } while (solver.info()!=Success && i<100); - * \endcode + * \include BiCGSTAB_step_by_step.cpp * Note that such a step by step excution is slightly slower. * * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner |