diff options
Diffstat (limited to 'Eigen/src/IterativeLinearSolvers/ConjugateGradient.h')
| -rw-r--r-- | Eigen/src/IterativeLinearSolvers/ConjugateGradient.h | 44 |
1 files changed, 22 insertions, 22 deletions
diff --git a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h index a799c3ef5..10cd94783 100644 --- a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h +++ b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h @@ -60,29 +60,29 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x, } VectorType p(n); - p = precond.solve(residual); //initial search direction + p = precond.solve(residual); // initial search direction VectorType z(n), tmp(n); RealScalar absNew = numext::real(residual.dot(p)); // the square of the absolute value of r scaled by invM Index i = 0; while(i < maxIters) { - tmp.noalias() = mat * p; // the bottleneck of the algorithm + tmp.noalias() = mat * p; // the bottleneck of the algorithm - Scalar alpha = absNew / p.dot(tmp); // the amount we travel on dir - x += alpha * p; // update solution - residual -= alpha * tmp; // update residue + Scalar alpha = absNew / p.dot(tmp); // the amount we travel on dir + x += alpha * p; // update solution + residual -= alpha * tmp; // update residual residualNorm2 = residual.squaredNorm(); if(residualNorm2 < threshold) break; - z = precond.solve(residual); // approximately solve for "A z = residual" + z = precond.solve(residual); // approximately solve for "A z = residual" RealScalar absOld = absNew; absNew = numext::real(residual.dot(z)); // update the absolute value of r - RealScalar beta = absNew / absOld; // calculate the Gram-Schmidt value used to create the new search direction - p = z + beta * p; // update search direction + RealScalar beta = absNew / absOld; // calculate the Gram-Schmidt value used to create the new search direction + p = z + beta * p; // update search direction i++; } tol_error = sqrt(residualNorm2 / rhsNorm2); @@ -122,24 +122,24 @@ struct traits<ConjugateGradient<_MatrixType,_UpLo,_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 - * ConjugateGradient<SparseMatrix<double> > cg; - * cg.compute(A); - * x = cg.solve(b); - * std::cout << "#iterations: " << cg.iterations() << std::endl; - * std::cout << "estimated error: " << cg.error() << std::endl; - * // update b, and solve again - * x = cg.solve(b); - * \endcode + \code + int n = 10000; + VectorXd x(n), b(n); + SparseMatrix<double> A(n,n); + // fill A and b + ConjugateGradient<SparseMatrix<double> > cg; + cg.compute(A); + x = cg.solve(b); + std::cout << "#iterations: " << cg.iterations() << std::endl; + std::cout << "estimated error: " << cg.error() << std::endl; + // update b, and solve again + x = cg.solve(b); + \endcode * * By default the iterations start with x=0 as an initial guess of the solution. * One can control the start using the solveWithGuess() method. * - * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner + * \sa class LSCG, class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner */ template< typename _MatrixType, int _UpLo, typename _Preconditioner> class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> > |