utilities.h

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// ------------------------------------------------------------------------
//
// SPDX-License-Identifier: LGPL-2.1-or-later
// Copyright (C) 2017 - 2024 by the deal.II authors
//
// This file is part of the deal.II library.
//
// Part of the source code is dual licensed under Apache-2.0 WITH
// LLVM-exception OR LGPL-2.1-or-later. Detailed license information
// governing the source code and code contributions can be found in
// LICENSE.md and CONTRIBUTING.md at the top level directory of deal.II.
//
// ------------------------------------------------------------------------
 
#ifndef dealii_lac_utilities_h
#define dealii_lac_utilities_h
 
#include <deal.II/base/config.h>
 
#include <deal.II/base/exceptions.h>
#include <deal.II/base/signaling_nan.h>
 
#include <deal.II/lac/lapack_support.h>
#include <deal.II/lac/vector_memory.h>
 
#include <array>
#include <complex>
#include <limits>
 
DEAL_II_NAMESPACE_OPEN
 
namespace Utilities
{
  namespace LinearAlgebra
  {
    template <typename NumberType>
    std::array<NumberType, 3>
    givens_rotation(const NumberType &x, const NumberType &y);
 
    template <typename NumberType>
    std::array<NumberType, 3>
    hyperbolic_rotation(const NumberType &x, const NumberType &y);
 
    template <typename OperatorType, typename VectorType>
    double
    lanczos_largest_eigenvalue(const OperatorType       &H,
                               const VectorType         &v0,
                               const unsigned int        k,
                               VectorMemory<VectorType> &vector_memory,
                               std::vector<double>      *eigenvalues = nullptr);
 
    template <typename OperatorType, typename VectorType>
    void
    chebyshev_filter(VectorType                     &x,
                     const OperatorType             &H,
                     const unsigned int              n,
                     const std::pair<double, double> unwanted_spectrum,
                     const double                    tau,
                     VectorMemory<VectorType>       &vector_memory);
 
  } // namespace LinearAlgebra
 
} // namespace Utilities
 
 
/*------------------------- Implementation ----------------------------*/
 
#ifndef DOXYGEN
 
namespace internal
{
  namespace UtilitiesImplementation
  {
    // We want to avoid including our own LAPACK wrapper header in any external
    // headers to avoid possible conflicts with other packages that may define
    // their own such header. At the same time we want to be able to call some
    // LAPACK functions from the template functions below. To resolve both
    // problems define some extra wrappers here that can be in the header:
    template <typename Number>
    void
    call_stev(const char            jobz,
              const types::blas_int n,
              Number               *d,
              Number               *e,
              Number               *z,
              const types::blas_int ldz,
              Number               *work,
              types::blas_int      *info);
  } // namespace UtilitiesImplementation
} // namespace internal
 
namespace Utilities
{
  namespace LinearAlgebra
  {
    template <typename NumberType>
    std::array<std::complex<NumberType>, 3>
    hyperbolic_rotation(const std::complex<NumberType> & /*f*/,
                        const std::complex<NumberType> & /*g*/)
    {
      AssertThrow(false, ExcNotImplemented());
      std::array<NumberType, 3> res;
      return res;
    }
 
 
 
    template <typename NumberType>
    std::array<NumberType, 3>
    hyperbolic_rotation(const NumberType &f, const NumberType &g)
    {
      Assert(f != 0, ExcDivideByZero());
      const NumberType tau = g / f;
      AssertThrow(std::abs(tau) < 1.,
                  ExcMessage(
                    "real-valued Hyperbolic rotation does not exist for (" +
                    std::to_string(f) + "," + std::to_string(g) + ")"));
      const NumberType u =
        std::copysign(std::sqrt((1. - tau) * (1. + tau)),
                      f); // <-- more stable than std::sqrt(1.-tau*tau)
      std::array<NumberType, 3> csr;
      csr[0] = 1. / u;       // c
      csr[1] = csr[0] * tau; // s
      csr[2] = f * u;        // r
      return csr;
    }
 
 
 
    template <typename NumberType>
    std::array<std::complex<NumberType>, 3>
    givens_rotation(const std::complex<NumberType> & /*f*/,
                    const std::complex<NumberType> & /*g*/)
    {
      AssertThrow(false, ExcNotImplemented());
      std::array<NumberType, 3> res;
      return res;
    }
 
 
 
    template <typename NumberType>
    std::array<NumberType, 3>
    givens_rotation(const NumberType &f, const NumberType &g)
    {
      std::array<NumberType, 3> res;
      // naive calculation for "r" may overflow or underflow:
      // c =  x / \sqrt{x^2+y^2}
      // s = -y / \sqrt{x^2+y^2}
 
      // See Golub 2013, Matrix computations, Chapter 5.1.8
      // Algorithm 5.1.3
      // and
      // Anderson (2000),
      // Discontinuous Plane Rotations and the Symmetric Eigenvalue Problem.
      // LAPACK Working Note 150, University of Tennessee, UT-CS-00-454,
      // December 4, 2000.
      // Algorithm 4
      // We implement the latter below:
      if (g == NumberType())
        {
          res[0] = std::copysign(1., f);
          res[1] = NumberType();
          res[2] = std::abs(f);
        }
      else if (f == NumberType())
        {
          res[0] = NumberType();
          res[1] = std::copysign(1., g);
          res[2] = std::abs(g);
        }
      else if (std::abs(f) > std::abs(g))
        {
          const NumberType tau = g / f;
          const NumberType u   = std::copysign(std::sqrt(1. + tau * tau), f);
          res[0]               = 1. / u;       // c
          res[1]               = res[0] * tau; // s
          res[2]               = f * u;        // r
        }
      else
        {
          const NumberType tau = f / g;
          const NumberType u   = std::copysign(std::sqrt(1. + tau * tau), g);
          res[1]               = 1. / u;       // s
          res[0]               = res[1] * tau; // c
          res[2]               = g * u;        // r
        }
 
      return res;
    }
 
 
 
    template <typename OperatorType, typename VectorType>
    double
    lanczos_largest_eigenvalue(const OperatorType       &H,
                               const VectorType         &v0_,
                               const unsigned int        k,
                               VectorMemory<VectorType> &vector_memory,
                               std::vector<double>      *eigenvalues)
    {
      // Do k-step Lanczos:
 
      typename VectorMemory<VectorType>::Pointer v(vector_memory);
      typename VectorMemory<VectorType>::Pointer v0(vector_memory);
      typename VectorMemory<VectorType>::Pointer f(vector_memory);
 
      v->reinit(v0_);
      v0->reinit(v0_);
      f->reinit(v0_);
 
      // two vectors to store diagonal and subdiagonal of the Lanczos
      // matrix
      std::vector<double> diagonal;
      std::vector<double> subdiagonal;
 
      // 1. Normalize input vector
      (*v)     = v0_;
      double a = v->l2_norm();
      Assert(a != 0, ExcDivideByZero());
      (*v) *= 1. / a;
 
      // 2. Compute f = Hv; a = f*v; f <- f - av; T(0,0)=a;
      H.vmult(*f, *v);
      a = (*f) * (*v);
      f->add(-a, *v);
      diagonal.push_back(a);
 
      // 3. Loop over steps
      for (unsigned int i = 1; i < k; ++i)
        {
          // 4. L2 norm of f
          const double b = f->l2_norm();
          Assert(b != 0, ExcDivideByZero());
          // 5. v0 <- v; v <- f/b
          *v0 = *v;
          *v  = *f;
          (*v) *= 1. / b;
          // 6. f = Hv; f <- f - b v0;
          H.vmult(*f, *v);
          f->add(-b, *v0);
          // 7. a = f*v; f <- f - a v;
          a = (*f) * (*v);
          f->add(-a, *v);
          // 8. T(i,i-1) = T(i-1,i) = b;  T(i,i) = a;
          diagonal.push_back(a);
          subdiagonal.push_back(b);
        }
 
      Assert(diagonal.size() == k, ExcInternalError());
      Assert(subdiagonal.size() == k - 1, ExcInternalError());
 
      // Use Lapack dstev to get ||T||_2 norm, i.e. the largest eigenvalue
      // of T
      const types::blas_int n = k;
      std::vector<double>   Z;       // unused for eigenvalues-only ("N") job
      const types::blas_int ldz = 1; // ^^   (>=1)
      std::vector<double>   work;    // ^^
      types::blas_int       info;
      // call lapack_templates.h wrapper:
      ::internal::UtilitiesImplementation::call_stev('N',
                                                           n,
                                                           diagonal.data(),
                                                           subdiagonal.data(),
                                                           Z.data(),
                                                           ldz,
                                                           work.data(),
                                                           &info);
 
      Assert(info == 0, LAPACKSupport::ExcErrorCode("dstev", info));
 
      if (eigenvalues != nullptr)
        {
          eigenvalues->resize(diagonal.size());
          std::copy(diagonal.begin(), diagonal.end(), eigenvalues->begin());
        }
 
      // note that the largest eigenvalue of T is below the largest
      // eigenvalue of the operator.
      // return ||T||_2 + ||f||_2, although it is not guaranteed to be an upper
      // bound.
      return diagonal[k - 1] + f->l2_norm();
    }
 
 
    template <typename OperatorType, typename VectorType>
    void
    chebyshev_filter(VectorType                     &x,
                     const OperatorType             &op,
                     const unsigned int              degree,
                     const std::pair<double, double> unwanted_spectrum,
                     const double                    a_L,
                     VectorMemory<VectorType>       &vector_memory)
    {
      const double a = unwanted_spectrum.first;
      const double b = unwanted_spectrum.second;
      Assert(degree > 0, ExcMessage("Only positive degrees make sense."));
 
      const bool scale = numbers::is_finite(a_L);
      Assert(
        a < b,
        ExcMessage(
          "Lower bound of the unwanted spectrum should be smaller than the upper bound."));
 
      Assert(a_L <= a || a_L >= b || !scale,
             ExcMessage(
               "Scaling point should be outside of the unwanted spectrum."));
 
      // Setup auxiliary vectors:
      typename VectorMemory<VectorType>::Pointer p_y(vector_memory);
      typename VectorMemory<VectorType>::Pointer p_yn(vector_memory);
 
      p_y->reinit(x);
      p_yn->reinit(x);
 
      // convenience to avoid pointers
      VectorType &y  = *p_y;
      VectorType &yn = *p_yn;
 
      // Below is an implementation of
      // Algorithm 3.2 in Zhou et al, Journal of Computational Physics 274
      // (2014) 770-782 with **a bugfix for sigma1**. Here is the original
      // algorithm verbatim:
      //
      // [Y]=chebyshev_filter_scaled(X, m, a, b, aL).
      // e=(b-a)/2; c=(a+b)/2; σ=e/(c-aL); τ=2/σ;
      // Y=(H∗X-c∗X)∗(σ/e);
      // for i=2 to m do
      //   σnew =1/(τ - σ);
      //   Yt =(H∗Y - c∗Y)∗(2∗σnew/e)-(σ∗σnew)∗X;
      //   X =Y; Y =Yt; σ =σnew;
 
      const double e     = (b - a) / 2.;
      const double c     = (a + b) / 2.;
      const double alpha = 1. / e;
      const double beta  = -c / e;
 
      const double sigma1 =
        e / (a_L - c); // BUGFIX which is relevant for odd degrees
      double       sigma = scale ? sigma1 : 1.;
      const double tau   = 2. / sigma;
      op.vmult(y, x);
      y.sadd(alpha * sigma, beta * sigma, x);
 
      for (unsigned int i = 2; i <= degree; ++i)
        {
          const double sigma_new = scale ? 1. / (tau - sigma) : 1.;
          op.vmult(yn, y);
          yn.sadd(2. * alpha * sigma_new, 2. * beta * sigma_new, y);
          yn.add(-sigma * sigma_new, x);
          x.swap(y);
          y.swap(yn);
          sigma = sigma_new;
        }
 
      x.swap(y);
    }
 
  } // namespace LinearAlgebra
} // namespace Utilities
 
#endif
 
 
 
DEAL_II_NAMESPACE_CLOSE
 
 
#endif