diff options
Diffstat (limited to 'source/blender/physics/intern/ConstrainedConjugateGradient.h')
| -rw-r--r-- | source/blender/physics/intern/ConstrainedConjugateGradient.h | 23 |
1 files changed, 13 insertions, 10 deletions
diff --git a/source/blender/physics/intern/ConstrainedConjugateGradient.h b/source/blender/physics/intern/ConstrainedConjugateGradient.h index 8d375d00056..4a0a6ab298c 100644 --- a/source/blender/physics/intern/ConstrainedConjugateGradient.h +++ b/source/blender/physics/intern/ConstrainedConjugateGradient.h @@ -31,9 +31,11 @@ namespace internal { * \param rhs: The right hand side vector b * \param x: On input and initial solution, on output the computed solution. * \param precond: A preconditioner being able to efficiently solve for an - * approximation of Ax=b (regardless of b) - * \param iters: On input the max number of iteration, on output the number of performed iterations. - * \param tol_error: On input the tolerance error, on output an estimation of the relative error. + * approximation of Ax=b (regardless of b) + * \param iters: On input the max number of iteration, + * on output the number of performed iterations. + * \param tol_error: On input the tolerance error, + * on output an estimation of the relative error. */ template<typename MatrixType, typename Rhs, @@ -59,7 +61,7 @@ EIGEN_DONT_INLINE void constrained_conjugate_gradient(const MatrixType &mat, int n = mat.cols(); - VectorType residual = filter * (rhs - mat * x); //initial residual + VectorType residual = filter * (rhs - mat * x); // initial residual RealScalar rhsNorm2 = (filter * rhs).squaredNorm(); if (rhsNorm2 == 0) { @@ -78,7 +80,7 @@ EIGEN_DONT_INLINE void constrained_conjugate_gradient(const MatrixType &mat, } VectorType p(n); - p = filter * precond.solve(residual); //initial search direction + p = filter * precond.solve(residual); // initial search direction VectorType z(n), tmp(n); RealScalar absNew = numext::real( @@ -157,17 +159,18 @@ struct traits< /** \ingroup IterativeLinearSolvers_Module * \brief A conjugate gradient solver for sparse self-adjoint problems with additional constraints * - * This class allows to solve for A.x = b sparse linear problems using a conjugate gradient algorithm. - * The sparse matrix A must be selfadjoint. The vectors x and b can be either dense or sparse. + * This class allows to solve for A.x = b sparse linear problems using a conjugate gradient + * algorithm. The sparse matrix A must be selfadjoint. The vectors x and b can be either dense or + * sparse. * * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix. * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower * or Upper. Default is Lower. * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner * - * 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 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. * * This class can be used as the direct solver classes. Here is a typical usage example: * \code |