Ifpack2_Details_Chebyshev_def.hpp

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// @HEADER
// *****************************************************************************
//       Ifpack2: Templated Object-Oriented Algebraic Preconditioner Package
//
// Copyright 2009 NTESS and the Ifpack2 contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER

#ifndef IFPACK2_DETAILS_CHEBYSHEV_DEF_HPP
#define IFPACK2_DETAILS_CHEBYSHEV_DEF_HPP


#include "Ifpack2_PowerMethod.hpp"
#include "Ifpack2_Details_Chebyshev_decl.hpp"
#include "Ifpack2_Details_Chebyshev_Weights.hpp"
// #include "Ifpack2_Details_ScaledDampedResidual.hpp"
#include "Ifpack2_Details_ChebyshevKernel.hpp"
#include "Kokkos_ArithTraits.hpp"
#include "Teuchos_FancyOStream.hpp"
#include "Teuchos_oblackholestream.hpp"
#include "Tpetra_Details_residual.hpp"
#include "Teuchos_LAPACK.hpp"
#include "Ifpack2_Details_LapackSupportsScalar.hpp"
#include <cmath>
#include <iostream>

namespace Ifpack2 {
namespace Details {

namespace {  // (anonymous)

// We use this text a lot in error messages.
const char computeBeforeApplyReminder[] =
    "This means one of the following:\n"
    "  - you have not yet called compute() on this instance, or \n"
    "  - you didn't call compute() after calling setParameters().\n\n"
    "After creating an Ifpack2::Chebyshev instance,\n"
    "you must _always_ call compute() at least once before calling apply().\n"
    "After calling compute() once, you do not need to call it again,\n"
    "unless the matrix has changed or you have changed parameters\n"
    "(by calling setParameters()).";

}  // namespace

// ReciprocalThreshold stuff below needs to be in a namspace visible outside
// of this file
template <class XV, class SizeType = typename XV::size_type>
struct V_ReciprocalThresholdSelfFunctor {
  typedef typename XV::execution_space execution_space;
  typedef typename XV::non_const_value_type value_type;
  typedef SizeType size_type;
  typedef Kokkos::ArithTraits<value_type> KAT;
  typedef typename KAT::mag_type mag_type;

  XV X_;
  const value_type minVal_;
  const mag_type minValMag_;

  V_ReciprocalThresholdSelfFunctor(const XV& X,
                                   const value_type& min_val)
    : X_(X)
    , minVal_(min_val)
    , minValMag_(KAT::abs(min_val)) {}

  KOKKOS_INLINE_FUNCTION
  void operator()(const size_type& i) const {
    const mag_type X_i_abs = KAT::abs(X_[i]);

    if (X_i_abs < minValMag_) {
      X_[i] = minVal_;
    } else {
      X_[i] = KAT::one() / X_[i];
    }
  }
};

template <class XV, class SizeType = typename XV::size_type>
struct LocalReciprocalThreshold {
  static void
  compute(const XV& X,
          const typename XV::non_const_value_type& minVal) {
    typedef typename XV::execution_space execution_space;
    Kokkos::RangePolicy<execution_space, SizeType> policy(0, X.extent(0));
    V_ReciprocalThresholdSelfFunctor<XV, SizeType> op(X, minVal);
    Kokkos::parallel_for(policy, op);
  }
};

template <class TpetraVectorType,
          const bool classic = TpetraVectorType::node_type::classic>
struct GlobalReciprocalThreshold {};

template <class TpetraVectorType>
struct GlobalReciprocalThreshold<TpetraVectorType, true> {
  static void
  compute(TpetraVectorType& V,
          const typename TpetraVectorType::scalar_type& min_val) {
    typedef typename TpetraVectorType::scalar_type scalar_type;
    typedef typename TpetraVectorType::mag_type mag_type;
    typedef Kokkos::ArithTraits<scalar_type> STS;

    const scalar_type ONE      = STS::one();
    const mag_type min_val_abs = STS::abs(min_val);

    Teuchos::ArrayRCP<scalar_type> V_0 = V.getDataNonConst(0);
    const size_t lclNumRows            = V.getLocalLength();

    for (size_t i = 0; i < lclNumRows; ++i) {
      const scalar_type V_0i = V_0[i];
      if (STS::abs(V_0i) < min_val_abs) {
        V_0[i] = min_val;
      } else {
        V_0[i] = ONE / V_0i;
      }
    }
  }
};

template <class TpetraVectorType>
struct GlobalReciprocalThreshold<TpetraVectorType, false> {
  static void
  compute(TpetraVectorType& X,
          const typename TpetraVectorType::scalar_type& minVal) {
    typedef typename TpetraVectorType::impl_scalar_type value_type;

    const value_type minValS = static_cast<value_type>(minVal);
    auto X_0                 = Kokkos::subview(X.getLocalViewDevice(Tpetra::Access::ReadWrite),
                                               Kokkos::ALL(), 0);
    LocalReciprocalThreshold<decltype(X_0)>::compute(X_0, minValS);
  }
};

// Utility function for inverting diagonal with threshold.
template <typename S, typename L, typename G, typename N>
void reciprocal_threshold(Tpetra::Vector<S, L, G, N>& V, const S& minVal) {
  GlobalReciprocalThreshold<Tpetra::Vector<S, L, G, N>>::compute(V, minVal);
}

template <class ScalarType, const bool lapackSupportsScalarType = LapackSupportsScalar<ScalarType>::value>
struct LapackHelper {
  static ScalarType
  tri_diag_spectral_radius(Teuchos::ArrayRCP<typename Teuchos::ScalarTraits<ScalarType>::magnitudeType> diag,
                           Teuchos::ArrayRCP<typename Teuchos::ScalarTraits<ScalarType>::magnitudeType> offdiag) {
    throw std::runtime_error("LAPACK does not support the scalar type.");
  }
};

template <class V>
void computeInitialGuessForCG(const V& diagonal, V& x) {
  using device_type  = typename V::node_type::device_type;
  using range_policy = Kokkos::RangePolicy<typename device_type::execution_space>;

  // Initial randomization of the vector
  x.randomize();

  // Zero the stuff that where the diagonal is equal to one.  These are assumed to
  // correspond to OAZ rows in the matrix.
  size_t N    = x.getLocalLength();
  auto d_view = diagonal.template getLocalView<device_type>(Tpetra::Access::ReadOnly);
  auto x_view = x.template getLocalView<device_type>(Tpetra::Access::ReadWrite);

  auto ONE  = Teuchos::ScalarTraits<typename V::impl_scalar_type>::one();
  auto ZERO = Teuchos::ScalarTraits<typename V::impl_scalar_type>::zero();

  Kokkos::parallel_for(
      "computeInitialGuessforCG::zero_bcs", range_policy(0, N), KOKKOS_LAMBDA(const size_t& i) {
        if (d_view(i, 0) == ONE)
          x_view(i, 0) = ZERO;
      });
}

template <class ScalarType>
struct LapackHelper<ScalarType, true> {
  static ScalarType
  tri_diag_spectral_radius(Teuchos::ArrayRCP<typename Teuchos::ScalarTraits<ScalarType>::magnitudeType> diag,
                           Teuchos::ArrayRCP<typename Teuchos::ScalarTraits<ScalarType>::magnitudeType> offdiag) {
    using STS           = Teuchos::ScalarTraits<ScalarType>;
    using MagnitudeType = typename STS::magnitudeType;
    int info            = 0;
    const int N         = diag.size();
    ScalarType scalar_dummy;
    std::vector<MagnitudeType> mag_dummy(4 * N);
    char char_N = 'N';

    // lambdaMin = one;
    ScalarType lambdaMax = STS::one();
    if (N > 2) {
      Teuchos::LAPACK<int, ScalarType> lapack;
      lapack.PTEQR(char_N, N, diag.getRawPtr(), offdiag.getRawPtr(),
                   &scalar_dummy, 1, &mag_dummy[0], &info);
      TEUCHOS_TEST_FOR_EXCEPTION(info < 0, std::logic_error,
                                 "Ifpack2::Details::LapackHelper::tri_diag_spectral_radius:"
                                 "LAPACK's _PTEQR failed with info = "
                                     << info << " < 0.  This suggests there might be a bug in the way Ifpack2 "
                                                "is calling LAPACK.  Please report this to the Ifpack2 developers.");
      // lambdaMin = Teuchos::as<ScalarType> (diag[N-1]);
      lambdaMax = Teuchos::as<ScalarType>(diag[0]);
    }
    return lambdaMax;
  }
};

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::checkInputMatrix() const {
  TEUCHOS_TEST_FOR_EXCEPTION(
      !A_.is_null() && A_->getGlobalNumRows() != A_->getGlobalNumCols(),
      std::invalid_argument,
      "Ifpack2::Chebyshev: The input matrix A must be square.  "
      "A has "
          << A_->getGlobalNumRows() << " rows and "
          << A_->getGlobalNumCols() << " columns.");

  // In debug mode, test that the domain and range Maps of the matrix
  // are the same.
  if (debug_ && !A_.is_null()) {
    Teuchos::RCP<const map_type> domainMap = A_->getDomainMap();
    Teuchos::RCP<const map_type> rangeMap  = A_->getRangeMap();

    // isSameAs is a collective, but if the two pointers are the same,
    // isSameAs will assume that they are the same on all processes, and
    // return true without an all-reduce.
    TEUCHOS_TEST_FOR_EXCEPTION(
        !domainMap->isSameAs(*rangeMap), std::invalid_argument,
        "Ifpack2::Chebyshev: The domain Map and range Map of the matrix must be "
        "the same (in the sense of isSameAs())."
            << std::endl
            << "We only check "
               "for this in debug mode.");
  }
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    checkConstructorInput() const {
  // mfh 12 Aug 2016: The if statement avoids an "unreachable
  // statement" warning for the checkInputMatrix() call, when
  // STS::isComplex is false.
  if (STS::isComplex) {
    TEUCHOS_TEST_FOR_EXCEPTION(true, std::logic_error,
                               "Ifpack2::Chebyshev: This class' implementation "
                               "of Chebyshev iteration only works for real-valued, symmetric positive "
                               "definite matrices.  However, you instantiated this class for ScalarType"
                               " = "
                                   << Teuchos::TypeNameTraits<ScalarType>::name() << ", which is a "
                                                                                     "complex-valued type.  While this may be algorithmically correct if all "
                                                                                     "of the complex numbers in the matrix have zero imaginary part, we "
                                                                                     "forbid using complex ScalarType altogether in order to remind you of "
                                                                                     "the limitations of our implementation (and of the algorithm itself).");
  } else {
    checkInputMatrix();
  }
}

template <class ScalarType, class MV>
Chebyshev<ScalarType, MV>::
    Chebyshev(Teuchos::RCP<const row_matrix_type> A)
  : A_(A)
  , savedDiagOffsets_(false)
  , computedLambdaMax_(STS::nan())
  , computedLambdaMin_(STS::nan())
  , lambdaMaxForApply_(STS::nan())
  , lambdaMinForApply_(STS::nan())
  , eigRatioForApply_(STS::nan())
  , userLambdaMax_(STS::nan())
  , userLambdaMin_(STS::nan())
  , userEigRatio_(Teuchos::as<ST>(30))
  , minDiagVal_(STS::eps())
  , numIters_(1)
  , eigMaxIters_(10)
  , eigRelTolerance_(Teuchos::ScalarTraits<MT>::zero())
  , eigKeepVectors_(false)
  , eigenAnalysisType_("power method")
  , eigNormalizationFreq_(1)
  , zeroStartingSolution_(true)
  , assumeMatrixUnchanged_(false)
  , chebyshevAlgorithm_("first")
  , computeMaxResNorm_(false)
  , computeSpectralRadius_(true)
  , ckUseNativeSpMV_(MV::node_type::is_gpu)
  , debug_(false) {
  checkConstructorInput();
}

template <class ScalarType, class MV>
Chebyshev<ScalarType, MV>::
    Chebyshev(Teuchos::RCP<const row_matrix_type> A,
              Teuchos::ParameterList& params)
  : A_(A)
  , savedDiagOffsets_(false)
  , computedLambdaMax_(STS::nan())
  , computedLambdaMin_(STS::nan())
  , lambdaMaxForApply_(STS::nan())
  , boostFactor_(static_cast<MT>(1.1))
  , lambdaMinForApply_(STS::nan())
  , eigRatioForApply_(STS::nan())
  , userLambdaMax_(STS::nan())
  , userLambdaMin_(STS::nan())
  , userEigRatio_(Teuchos::as<ST>(30))
  , minDiagVal_(STS::eps())
  , numIters_(1)
  , eigMaxIters_(10)
  , eigRelTolerance_(Teuchos::ScalarTraits<MT>::zero())
  , eigKeepVectors_(false)
  , eigenAnalysisType_("power method")
  , eigNormalizationFreq_(1)
  , zeroStartingSolution_(true)
  , assumeMatrixUnchanged_(false)
  , chebyshevAlgorithm_("first")
  , computeMaxResNorm_(false)
  , computeSpectralRadius_(true)
  , ckUseNativeSpMV_(MV::node_type::is_gpu)
  , debug_(false) {
  checkConstructorInput();
  setParameters(params);
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    setParameters(Teuchos::ParameterList& plist) {
  using Teuchos::RCP;
  using Teuchos::rcp;
  using Teuchos::rcp_const_cast;

  // Note to developers: The logic for this method is complicated,
  // because we want to accept Ifpack and ML parameters whenever
  // possible, but we don't want to add their default values to the
  // user's ParameterList.  That's why we do all the isParameter()
  // checks, instead of using the two-argument version of get()
  // everywhere.  The min and max eigenvalue parameters are also a
  // special case, because we decide whether or not to do eigenvalue
  // analysis based on whether the user supplied the max eigenvalue.

  // Default values of all the parameters.
  const ST defaultLambdaMax = STS::nan();
  const ST defaultLambdaMin = STS::nan();
  // 30 is Ifpack::Chebyshev's default.  ML has a different default
  // eigRatio for smoothers and the coarse-grid solve (if using
  // Chebyshev for that).  The former uses 20; the latter uses 30.
  // We're testing smoothers here, so use 20.  (However, if you give
  // ML an Epetra matrix, it will use Ifpack for Chebyshev, in which
  // case it would defer to Ifpack's default settings.)
  const ST defaultEigRatio                    = Teuchos::as<ST>(30);
  const MT defaultBoostFactor                 = static_cast<MT>(1.1);
  const ST defaultMinDiagVal                  = STS::eps();
  const int defaultNumIters                   = 1;
  const int defaultEigMaxIters                = 10;
  const MT defaultEigRelTolerance             = Teuchos::ScalarTraits<MT>::zero();
  const bool defaultEigKeepVectors            = false;
  const int defaultEigNormalizationFreq       = 1;
  const bool defaultZeroStartingSolution      = true;  // Ifpack::Chebyshev default
  const bool defaultAssumeMatrixUnchanged     = false;
  const std::string defaultChebyshevAlgorithm = "first";
  const bool defaultComputeMaxResNorm         = false;
  const bool defaultComputeSpectralRadius     = true;
  const bool defaultCkUseNativeSpMV           = MV::node_type::is_gpu;
  const bool defaultDebug                     = false;

  // We'll set the instance data transactionally, after all reads
  // from the ParameterList.  That way, if any of the ParameterList
  // reads fail (e.g., due to the wrong parameter type), we will not
  // have left the instance data in a half-changed state.
  RCP<const V> userInvDiagCopy;  // if nonnull: deep copy of user's Vector
  ST lambdaMax                   = defaultLambdaMax;
  ST lambdaMin                   = defaultLambdaMin;
  ST eigRatio                    = defaultEigRatio;
  MT boostFactor                 = defaultBoostFactor;
  ST minDiagVal                  = defaultMinDiagVal;
  int numIters                   = defaultNumIters;
  int eigMaxIters                = defaultEigMaxIters;
  MT eigRelTolerance             = defaultEigRelTolerance;
  bool eigKeepVectors            = defaultEigKeepVectors;
  int eigNormalizationFreq       = defaultEigNormalizationFreq;
  bool zeroStartingSolution      = defaultZeroStartingSolution;
  bool assumeMatrixUnchanged     = defaultAssumeMatrixUnchanged;
  std::string chebyshevAlgorithm = defaultChebyshevAlgorithm;
  bool computeMaxResNorm         = defaultComputeMaxResNorm;
  bool computeSpectralRadius     = defaultComputeSpectralRadius;
  bool ckUseNativeSpMV           = defaultCkUseNativeSpMV;
  bool debug                     = defaultDebug;

  // Fetch the parameters from the ParameterList.  Defer all
  // externally visible side effects until we have finished all
  // ParameterList interaction.  This makes the method satisfy the
  // strong exception guarantee.

  if (plist.isType<bool>("debug")) {
    debug = plist.get<bool>("debug");
  } else if (plist.isType<int>("debug")) {
    const int debugInt = plist.get<bool>("debug");
    debug              = debugInt != 0;
  }

  // Get the user-supplied inverse diagonal.
  //
  // Check for a raw pointer (const V* or V*), for Ifpack
  // compatibility, as well as for RCP<const V>, RCP<V>, const V, or
  // V.  We'll make a deep copy of the vector at the end of this
  // method anyway, so its const-ness doesn't matter.  We handle the
  // latter two cases ("const V" or "V") specially (copy them into
  // userInvDiagCopy first, which is otherwise null at the end of the
  // long if-then chain) to avoid an extra copy.

  const char opInvDiagLabel[] = "chebyshev: operator inv diagonal";
  if (plist.isParameter(opInvDiagLabel)) {
    // Pointer to the user's Vector, if provided.
    RCP<const V> userInvDiag;

    if (plist.isType<const V*>(opInvDiagLabel)) {
      const V* rawUserInvDiag =
          plist.get<const V*>(opInvDiagLabel);
      // Nonowning reference (we'll make a deep copy below)
      userInvDiag = rcp(rawUserInvDiag, false);
    } else if (plist.isType<const V*>(opInvDiagLabel)) {
      V* rawUserInvDiag = plist.get<V*>(opInvDiagLabel);
      // Nonowning reference (we'll make a deep copy below)
      userInvDiag = rcp(const_cast<const V*>(rawUserInvDiag), false);
    } else if (plist.isType<RCP<const V>>(opInvDiagLabel)) {
      userInvDiag = plist.get<RCP<const V>>(opInvDiagLabel);
    } else if (plist.isType<RCP<V>>(opInvDiagLabel)) {
      RCP<V> userInvDiagNonConst =
          plist.get<RCP<V>>(opInvDiagLabel);
      userInvDiag = rcp_const_cast<const V>(userInvDiagNonConst);
    } else if (plist.isType<const V>(opInvDiagLabel)) {
      const V& userInvDiagRef = plist.get<const V>(opInvDiagLabel);
      userInvDiagCopy         = rcp(new V(userInvDiagRef, Teuchos::Copy));
      userInvDiag             = userInvDiagCopy;
    } else if (plist.isType<V>(opInvDiagLabel)) {
      V& userInvDiagNonConstRef = plist.get<V>(opInvDiagLabel);
      const V& userInvDiagRef   = const_cast<const V&>(userInvDiagNonConstRef);
      userInvDiagCopy           = rcp(new V(userInvDiagRef, Teuchos::Copy));
      userInvDiag               = userInvDiagCopy;
    }

    // NOTE: If the user's parameter has some strange type that we
    // didn't test above, userInvDiag might still be null.  You may
    // want to add an error test for this condition.  Currently, we
    // just say in this case that the user didn't give us a Vector.

    // If we have userInvDiag but don't have a deep copy yet, make a
    // deep copy now.
    if (!userInvDiag.is_null() && userInvDiagCopy.is_null()) {
      userInvDiagCopy = rcp(new V(*userInvDiag, Teuchos::Copy));
    }

    // NOTE: userInvDiag, if provided, is a row Map version of the
    // Vector.  We don't necessarily have a range Map yet.  compute()
    // would be the proper place to compute the range Map version of
    // userInvDiag.
  }

  // Load the kernel fuse override from the parameter list
  if (plist.isParameter("chebyshev: use native spmv"))
    ckUseNativeSpMV = plist.get("chebyshev: use native spmv", ckUseNativeSpMV);

  // Don't fill in defaults for the max or min eigenvalue, because
  // this class uses the existence of those parameters to determine
  // whether it should do eigenanalysis.
  if (plist.isParameter("chebyshev: max eigenvalue")) {
    if (plist.isType<double>("chebyshev: max eigenvalue"))
      lambdaMax = plist.get<double>("chebyshev: max eigenvalue");
    else
      lambdaMax = plist.get<ST>("chebyshev: max eigenvalue");
    TEUCHOS_TEST_FOR_EXCEPTION(
        STS::isnaninf(lambdaMax), std::invalid_argument,
        "Ifpack2::Chebyshev::setParameters: \"chebyshev: max eigenvalue\" "
        "parameter is NaN or Inf.  This parameter is optional, but if you "
        "choose to supply it, it must have a finite value.");
  }
  if (plist.isParameter("chebyshev: min eigenvalue")) {
    if (plist.isType<double>("chebyshev: min eigenvalue"))
      lambdaMin = plist.get<double>("chebyshev: min eigenvalue");
    else
      lambdaMin = plist.get<ST>("chebyshev: min eigenvalue");
    TEUCHOS_TEST_FOR_EXCEPTION(
        STS::isnaninf(lambdaMin), std::invalid_argument,
        "Ifpack2::Chebyshev::setParameters: \"chebyshev: min eigenvalue\" "
        "parameter is NaN or Inf.  This parameter is optional, but if you "
        "choose to supply it, it must have a finite value.");
  }

  // Only fill in Ifpack2's name for the default parameter, not ML's.
  if (plist.isParameter("smoother: Chebyshev alpha")) {  // ML compatibility
    if (plist.isType<double>("smoother: Chebyshev alpha"))
      eigRatio = plist.get<double>("smoother: Chebyshev alpha");
    else
      eigRatio = plist.get<ST>("smoother: Chebyshev alpha");
  }
  // Ifpack2's name overrides ML's name.
  eigRatio = plist.get("chebyshev: ratio eigenvalue", eigRatio);
  TEUCHOS_TEST_FOR_EXCEPTION(
      STS::isnaninf(eigRatio), std::invalid_argument,
      "Ifpack2::Chebyshev::setParameters: \"chebyshev: ratio eigenvalue\" "
      "parameter (also called \"smoother: Chebyshev alpha\") is NaN or Inf.  "
      "This parameter is optional, but if you choose to supply it, it must have "
      "a finite value.");
  // mfh 11 Feb 2013: This class is currently only correct for real
  // Scalar types, but we still want it to build for complex Scalar
  // type so that users of Ifpack2::Factory can build their
  // executables for real or complex Scalar type.  Thus, we take the
  // real parts here, which are always less-than comparable.
  TEUCHOS_TEST_FOR_EXCEPTION(
      STS::real(eigRatio) < STS::real(STS::one()),
      std::invalid_argument,
      "Ifpack2::Chebyshev::setParameters: \"chebyshev: ratio eigenvalue\""
      "parameter (also called \"smoother: Chebyshev alpha\") must be >= 1, "
      "but you supplied the value "
          << eigRatio << ".");

  // See Github Issue #234.  This parameter may be either MT
  // (preferred) or double.  We check both.
  {
    const char paramName[] = "chebyshev: boost factor";

    if (plist.isParameter(paramName)) {
      if (plist.isType<MT>(paramName)) {  // MT preferred
        boostFactor = plist.get<MT>(paramName);
      } else if (!std::is_same<double, MT>::value &&
                 plist.isType<double>(paramName)) {
        const double dblBF = plist.get<double>(paramName);
        boostFactor        = static_cast<MT>(dblBF);
      } else {
        TEUCHOS_TEST_FOR_EXCEPTION(true, std::invalid_argument,
                                   "Ifpack2::Chebyshev::setParameters: \"chebyshev: boost factor\""
                                   "parameter must have type magnitude_type (MT) or double.");
      }
    } else {  // parameter not in the list
      // mfh 12 Aug 2016: To preserve current behavior (that fills in
      // any parameters not in the input ParameterList with their
      // default values), we call set() here.  I don't actually like
      // this behavior; I prefer the Belos model, where the input
      // ParameterList is a delta from current behavior.  However, I
      // don't want to break things.
      plist.set(paramName, defaultBoostFactor);
    }
    TEUCHOS_TEST_FOR_EXCEPTION(boostFactor < Teuchos::ScalarTraits<MT>::one(), std::invalid_argument,
                               "Ifpack2::Chebyshev::setParameters: \"" << paramName << "\" parameter "
                                                                                       "must be >= 1, but you supplied the value "
                                                                       << boostFactor << ".");
  }

  // Same name in Ifpack2 and Ifpack.
  minDiagVal = plist.get("chebyshev: min diagonal value", minDiagVal);
  TEUCHOS_TEST_FOR_EXCEPTION(
      STS::isnaninf(minDiagVal), std::invalid_argument,
      "Ifpack2::Chebyshev::setParameters: \"chebyshev: min diagonal value\" "
      "parameter is NaN or Inf.  This parameter is optional, but if you choose "
      "to supply it, it must have a finite value.");

  // Only fill in Ifpack2's name, not ML's or Ifpack's.
  if (plist.isParameter("smoother: sweeps")) {  // ML compatibility
    numIters = plist.get<int>("smoother: sweeps");
  }                                               // Ifpack's name overrides ML's name.
  if (plist.isParameter("relaxation: sweeps")) {  // Ifpack compatibility
    numIters = plist.get<int>("relaxation: sweeps");
  }  // Ifpack2's name overrides Ifpack's name.
  numIters = plist.get("chebyshev: degree", numIters);
  TEUCHOS_TEST_FOR_EXCEPTION(
      numIters < 0, std::invalid_argument,
      "Ifpack2::Chebyshev::setParameters: \"chebyshev: degree\" parameter (also "
      "called \"smoother: sweeps\" or \"relaxation: sweeps\") must be a "
      "nonnegative integer.  You gave a value of "
          << numIters << ".");

  // The last parameter name overrides the first.
  if (plist.isParameter("eigen-analysis: iterations")) {  // ML compatibility
    eigMaxIters = plist.get<int>("eigen-analysis: iterations");
  }  // Ifpack2's name overrides ML's name.
  eigMaxIters = plist.get("chebyshev: eigenvalue max iterations", eigMaxIters);
  TEUCHOS_TEST_FOR_EXCEPTION(
      eigMaxIters < 0, std::invalid_argument,
      "Ifpack2::Chebyshev::setParameters: \"chebyshev: eigenvalue max iterations"
      "\" parameter (also called \"eigen-analysis: iterations\") must be a "
      "nonnegative integer.  You gave a value of "
          << eigMaxIters << ".");

  if (plist.isType<double>("chebyshev: eigenvalue relative tolerance"))
    eigRelTolerance = Teuchos::as<MT>(plist.get<double>("chebyshev: eigenvalue relative tolerance"));
  else if (plist.isType<MT>("chebyshev: eigenvalue relative tolerance"))
    eigRelTolerance = plist.get<MT>("chebyshev: eigenvalue relative tolerance");
  else if (plist.isType<ST>("chebyshev: eigenvalue relative tolerance"))
    eigRelTolerance = Teuchos::ScalarTraits<ST>::magnitude(plist.get<ST>("chebyshev: eigenvalue relative tolerance"));

  eigKeepVectors = plist.get("chebyshev: eigenvalue keep vectors", eigKeepVectors);

  eigNormalizationFreq = plist.get("chebyshev: eigenvalue normalization frequency", eigNormalizationFreq);
  TEUCHOS_TEST_FOR_EXCEPTION(
      eigNormalizationFreq < 0, std::invalid_argument,
      "Ifpack2::Chebyshev::setParameters: \"chebyshev: eigenvalue normalization frequency"
      "\" parameter must be a "
      "nonnegative integer.  You gave a value of "
          << eigNormalizationFreq << ".")

  zeroStartingSolution  = plist.get("chebyshev: zero starting solution",
                                    zeroStartingSolution);
  assumeMatrixUnchanged = plist.get("chebyshev: assume matrix does not change",
                                    assumeMatrixUnchanged);

  // We don't want to fill these parameters in, because they shouldn't
  // be visible to Ifpack2::Chebyshev users.
  if (plist.isParameter("chebyshev: algorithm")) {
    chebyshevAlgorithm = plist.get<std::string>("chebyshev: algorithm");
    TEUCHOS_TEST_FOR_EXCEPTION(
        chebyshevAlgorithm != "first" &&
            chebyshevAlgorithm != "textbook" &&
            chebyshevAlgorithm != "fourth" &&
            chebyshevAlgorithm != "opt_fourth",
        std::invalid_argument,
        "Ifpack2::Chebyshev: Ifpack2 only supports \"first\", \"textbook\", \"fourth\", and \"opt_fourth\", for \"chebyshev: algorithm\".");
  }

#ifdef IFPACK2_ENABLE_DEPRECATED_CODE
  // to preserve behavior with previous input decks, only read "chebyshev:textbook algorithm" setting
  // if a user has not specified "chebyshev: algorithm"
  if (!plist.isParameter("chebyshev: algorithm")) {
    if (plist.isParameter("chebyshev: textbook algorithm")) {
      const bool textbookAlgorithm = plist.get<bool>("chebyshev: textbook algorithm");
      if (textbookAlgorithm) {
        chebyshevAlgorithm = "textbook";
      } else {
        chebyshevAlgorithm = "first";
      }
    }
  }
#endif

  if (plist.isParameter("chebyshev: compute max residual norm")) {
    computeMaxResNorm = plist.get<bool>("chebyshev: compute max residual norm");
  }
  if (plist.isParameter("chebyshev: compute spectral radius")) {
    computeSpectralRadius = plist.get<bool>("chebyshev: compute spectral radius");
  }

  // Test for Ifpack parameters that we won't ever implement here.
  // Be careful to use the one-argument version of get(), since the
  // two-argment version adds the parameter if it's not there.
  TEUCHOS_TEST_FOR_EXCEPTION(plist.isType<bool>("chebyshev: use block mode") &&
                                 !plist.get<bool>("chebyshev: use block mode"),
                             std::invalid_argument,
                             "Ifpack2::Chebyshev requires that if you set \"chebyshev: use "
                             "block mode\" at all, you must set it to false.  "
                             "Ifpack2::Chebyshev does not implement Ifpack's block mode.");
  TEUCHOS_TEST_FOR_EXCEPTION(plist.isType<bool>("chebyshev: solve normal equations") &&
                                 !plist.get<bool>("chebyshev: solve normal equations"),
                             std::invalid_argument,
                             "Ifpack2::Chebyshev does not and will never implement the Ifpack "
                             "parameter \"chebyshev: solve normal equations\".  If you want to "
                             "solve the normal equations, construct a Tpetra::Operator that "
                             "implements A^* A, and use Chebyshev to solve A^* A x = A^* b.");

  // Test for Ifpack parameters that we haven't implemented yet.
  //
  // For now, we only check that this ML parameter, if provided, has
  // the one value that we support.  We consider other values "invalid
  // arguments" rather than "logic errors," because Ifpack does not
  // implement eigenanalyses other than the power method.
  std::string eigenAnalysisType("power-method");
  if (plist.isParameter("eigen-analysis: type")) {
    eigenAnalysisType = plist.get<std::string>("eigen-analysis: type");
    TEUCHOS_TEST_FOR_EXCEPTION(
        eigenAnalysisType != "power-method" &&
            eigenAnalysisType != "power method" &&
            eigenAnalysisType != "cg",
        std::invalid_argument,
        "Ifpack2::Chebyshev: Ifpack2 only supports \"power method\" and \"cg\" for \"eigen-analysis: type\".");
  }

  // We've validated all the parameters, so it's safe now to "commit" them.
  userInvDiag_           = userInvDiagCopy;
  userLambdaMax_         = lambdaMax;
  userLambdaMin_         = lambdaMin;
  userEigRatio_          = eigRatio;
  boostFactor_           = static_cast<MT>(boostFactor);
  minDiagVal_            = minDiagVal;
  numIters_              = numIters;
  eigMaxIters_           = eigMaxIters;
  eigRelTolerance_       = eigRelTolerance;
  eigKeepVectors_        = eigKeepVectors;
  eigNormalizationFreq_  = eigNormalizationFreq;
  eigenAnalysisType_     = eigenAnalysisType;
  zeroStartingSolution_  = zeroStartingSolution;
  assumeMatrixUnchanged_ = assumeMatrixUnchanged;
  chebyshevAlgorithm_    = chebyshevAlgorithm;
  computeMaxResNorm_     = computeMaxResNorm;
  computeSpectralRadius_ = computeSpectralRadius;
  ckUseNativeSpMV_       = ckUseNativeSpMV;
  debug_                 = debug;

  if (debug_) {
    // Only print if myRank == 0.
    int myRank = -1;
    if (A_.is_null() || A_->getComm().is_null()) {
      // We don't have a communicator (yet), so we assume that
      // everybody can print.  Revise this expectation in setMatrix().
      myRank = 0;
    } else {
      myRank = A_->getComm()->getRank();
    }

    if (myRank == 0) {
      out_ = Teuchos::getFancyOStream(Teuchos::rcpFromRef(std::cerr));
    } else {
      using Teuchos::oblackholestream;  // prints nothing
      RCP<oblackholestream> blackHole(new oblackholestream());
      out_ = Teuchos::getFancyOStream(blackHole);
    }
  } else {  // NOT debug
    // free the "old" output stream, if there was one
    out_ = Teuchos::null;
  }
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::reset() {
  ck_                = Teuchos::null;
  D_                 = Teuchos::null;
  diagOffsets_       = offsets_type();
  savedDiagOffsets_  = false;
  W_                 = Teuchos::null;
  computedLambdaMax_ = STS::nan();
  computedLambdaMin_ = STS::nan();
  eigVector_         = Teuchos::null;
  eigVector2_        = Teuchos::null;
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    setMatrix(const Teuchos::RCP<const row_matrix_type>& A) {
  if (A.getRawPtr() != A_.getRawPtr()) {
    if (!assumeMatrixUnchanged_) {
      reset();
    }
    A_  = A;
    ck_ = Teuchos::null;  // constructed on demand

    // The communicator may have changed, or we may not have had a
    // communicator before.  Thus, we may have to reset the debug
    // output stream.
    if (debug_) {
      // Only print if myRank == 0.
      int myRank = -1;
      if (A.is_null() || A->getComm().is_null()) {
        // We don't have a communicator (yet), so we assume that
        // everybody can print.  Revise this expectation in setMatrix().
        myRank = 0;
      } else {
        myRank = A->getComm()->getRank();
      }

      if (myRank == 0) {
        out_ = Teuchos::getFancyOStream(Teuchos::rcpFromRef(std::cerr));
      } else {
        Teuchos::RCP<Teuchos::oblackholestream> blackHole(new Teuchos::oblackholestream());
        out_ = Teuchos::getFancyOStream(blackHole);  // print nothing on other processes
      }
    } else {  // NOT debug
      // free the "old" output stream, if there was one
      out_ = Teuchos::null;
    }
  }
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::compute() {
  using std::endl;
  // Some of the optimizations below only work if A_ is a
  // Tpetra::CrsMatrix.  We'll make our best guess about its type
  // here, since we have no way to get back the original fifth
  // template parameter.
  typedef Tpetra::CrsMatrix<typename MV::scalar_type,
                            typename MV::local_ordinal_type,
                            typename MV::global_ordinal_type,
                            typename MV::node_type>
      crs_matrix_type;

  TEUCHOS_TEST_FOR_EXCEPTION(
      A_.is_null(), std::runtime_error,
      "Ifpack2::Chebyshev::compute: The input "
      "matrix A is null.  Please call setMatrix() with a nonnull input matrix "
      "before calling this method.");

  // If A_ is a CrsMatrix and its graph is constant, we presume that
  // the user plans to reuse the structure of A_, but possibly change
  // A_'s values before each compute() call.  This is the intended use
  // case for caching the offsets of the diagonal entries of A_, to
  // speed up extraction of diagonal entries on subsequent compute()
  // calls.

  // FIXME (mfh 22 Jan 2013, 10 Feb 2013) In all cases when we use
  // isnaninf() in this method, we really only want to check if the
  // number is NaN.  Inf means something different.  However,
  // Teuchos::ScalarTraits doesn't distinguish the two cases.

  // makeInverseDiagonal() returns a range Map Vector.
  if (userInvDiag_.is_null()) {
    Teuchos::RCP<const crs_matrix_type> A_crsMat =
        Teuchos::rcp_dynamic_cast<const crs_matrix_type>(A_);
    if (D_.is_null()) {  // We haven't computed D_ before
      if (!A_crsMat.is_null() && A_crsMat->isFillComplete()) {
        // It's a CrsMatrix with a const graph; cache diagonal offsets.
        const size_t lclNumRows = A_crsMat->getLocalNumRows();
        if (diagOffsets_.extent(0) < lclNumRows) {
          diagOffsets_ = offsets_type();  // clear first to save memory
          diagOffsets_ = offsets_type("offsets", lclNumRows);
        }
        A_crsMat->getCrsGraph()->getLocalDiagOffsets(diagOffsets_);
        savedDiagOffsets_ = true;
        D_                = makeInverseDiagonal(*A_, true);
      } else {  // either A_ is not a CrsMatrix, or its graph is nonconst
        D_ = makeInverseDiagonal(*A_);
      }
    } else if (!assumeMatrixUnchanged_) {  // D_ exists but A_ may have changed
      if (!A_crsMat.is_null() && A_crsMat->isFillComplete()) {
        // It's a CrsMatrix with a const graph; cache diagonal offsets
        // if we haven't already.
        if (!savedDiagOffsets_) {
          const size_t lclNumRows = A_crsMat->getLocalNumRows();
          if (diagOffsets_.extent(0) < lclNumRows) {
            diagOffsets_ = offsets_type();  // clear first to save memory
            diagOffsets_ = offsets_type("offsets", lclNumRows);
          }
          A_crsMat->getCrsGraph()->getLocalDiagOffsets(diagOffsets_);
          savedDiagOffsets_ = true;
        }
        // Now we're guaranteed to have cached diagonal offsets.
        D_ = makeInverseDiagonal(*A_, true);
      } else {  // either A_ is not a CrsMatrix, or its graph is nonconst
        D_ = makeInverseDiagonal(*A_);
      }
    }
  } else {  // the user provided an inverse diagonal
    D_ = makeRangeMapVectorConst(userInvDiag_);
  }

  // Have we estimated eigenvalues before?
  const bool computedEigenvalueEstimates =
      STS::isnaninf(computedLambdaMax_) || STS::isnaninf(computedLambdaMin_);

  // Only recompute the eigenvalue estimates if
  // - we are supposed to assume that the matrix may have changed, or
  // - they haven't been computed before, and the user hasn't given
  //   us at least an estimate of the max eigenvalue.
  //
  // We at least need an estimate of the max eigenvalue.  This is the
  // most important one if using Chebyshev as a smoother.

  if (!assumeMatrixUnchanged_ ||
      (!computedEigenvalueEstimates && STS::isnaninf(userLambdaMax_))) {
    ST computedLambdaMax;
    if ((eigenAnalysisType_ == "power method") || (eigenAnalysisType_ == "power-method")) {
      Teuchos::RCP<V> x;
      if (eigVector_.is_null()) {
        x = Teuchos::rcp(new V(A_->getDomainMap()));
        if (eigKeepVectors_)
          eigVector_ = x;
        PowerMethod::computeInitialGuessForPowerMethod(*x, false);
      } else
        x = eigVector_;

      Teuchos::RCP<V> y;
      if (eigVector2_.is_null()) {
        y = rcp(new V(A_->getRangeMap()));
        if (eigKeepVectors_)
          eigVector2_ = y;
      } else
        y = eigVector2_;

      Teuchos::RCP<Teuchos::FancyOStream> stream = (debug_ ? out_ : Teuchos::null);
      computedLambdaMax                          = PowerMethod::powerMethodWithInitGuess(*A_, *D_, eigMaxIters_, x, y,
                                                                                         eigRelTolerance_, eigNormalizationFreq_, stream,
                                                                                         computeSpectralRadius_);
    } else {
      computedLambdaMax = cgMethod(*A_, *D_, eigMaxIters_);
    }
    TEUCHOS_TEST_FOR_EXCEPTION(
        STS::isnaninf(computedLambdaMax),
        std::runtime_error,
        "Ifpack2::Chebyshev::compute: Estimation of the max eigenvalue "
        "of D^{-1} A failed, by producing Inf or NaN.  This probably means that "
        "the matrix contains Inf or NaN values, or that it is badly scaled.");
    TEUCHOS_TEST_FOR_EXCEPTION(
        STS::isnaninf(userEigRatio_),
        std::logic_error,
        "Ifpack2::Chebyshev::compute: userEigRatio_ is Inf or NaN."
            << endl
            << "This should be impossible." << endl
            << "Please report this bug to the Ifpack2 developers.");

    // The power method doesn't estimate the min eigenvalue, so we
    // do our best to provide an estimate.  userEigRatio_ has a
    // reasonable default value, and if the user provided it, we
    // have already checked that its value is finite and >= 1.
    const ST computedLambdaMin = computedLambdaMax / userEigRatio_;

    // Defer "committing" results until all computations succeeded.
    computedLambdaMax_ = computedLambdaMax;
    computedLambdaMin_ = computedLambdaMin;
  } else {
    TEUCHOS_TEST_FOR_EXCEPTION(
        STS::isnaninf(userLambdaMax_) && STS::isnaninf(computedLambdaMax_),
        std::logic_error,
        "Ifpack2::Chebyshev::compute: " << endl
                                        << "Both userLambdaMax_ and computedLambdaMax_ are Inf or NaN."
                                        << endl
                                        << "This should be impossible." << endl
                                        << "Please report this bug to the Ifpack2 developers.");
  }

  // Figure out the eigenvalue estimates that apply() will use.

  // Always favor the user's max eigenvalue estimate, if provided.
  lambdaMaxForApply_ = STS::isnaninf(userLambdaMax_) ? computedLambdaMax_ : userLambdaMax_;

  // mfh 11 Feb 2013: For now, we imitate Ifpack by ignoring the
  // user's min eigenvalue estimate, and using the given eigenvalue
  // ratio to estimate the min eigenvalue.  We could instead do this:
  // favor the user's eigenvalue ratio estimate, but if it's not
  // provided, use lambdaMax / lambdaMin.  However, we want Chebyshev
  // to have sensible smoother behavior if the user did not provide
  // eigenvalue estimates.  Ifpack's behavior attempts to push down
  // the error terms associated with the largest eigenvalues, while
  // expecting that users will only want a small number of iterations,
  // so that error terms associated with the smallest eigenvalues
  // won't grow too much.  This is sensible behavior for a smoother.
  lambdaMinForApply_ = lambdaMaxForApply_ / userEigRatio_;
  eigRatioForApply_  = userEigRatio_;

  if (chebyshevAlgorithm_ == "first") {
    // Ifpack has a special-case modification of the eigenvalue bounds
    // for the case where the max eigenvalue estimate is close to one.
    const ST one = Teuchos::as<ST>(1);
    // FIXME (mfh 20 Nov 2013) Should scale this 1.0e-6 term
    // appropriately for MT's machine precision.
    if (STS::magnitude(lambdaMaxForApply_ - one) < Teuchos::as<MT>(1.0e-6)) {
      lambdaMinForApply_ = one;
      lambdaMaxForApply_ = lambdaMinForApply_;
      eigRatioForApply_  = one;  // Ifpack doesn't include this line.
    }
  }
}

template <class ScalarType, class MV>
ScalarType
Chebyshev<ScalarType, MV>::
    getLambdaMaxForApply() const {
  return lambdaMaxForApply_;
}

template <class ScalarType, class MV>
typename Chebyshev<ScalarType, MV>::MT
Chebyshev<ScalarType, MV>::apply(const MV& B, MV& X) {
  const char prefix[] = "Ifpack2::Chebyshev::apply: ";

  if (debug_) {
    *out_ << "apply: " << std::endl;
  }
  TEUCHOS_TEST_FOR_EXCEPTION(A_.is_null(), std::runtime_error, prefix << "The input matrix A is null. "
                                                                         " Please call setMatrix() with a nonnull input matrix, and then call "
                                                                         "compute(), before calling this method.");
  TEUCHOS_TEST_FOR_EXCEPTION(STS::isnaninf(lambdaMaxForApply_), std::runtime_error,
                             prefix << "There is no estimate for the max eigenvalue."
                                    << std::endl
                                    << computeBeforeApplyReminder);
  TEUCHOS_TEST_FOR_EXCEPTION(STS::isnaninf(lambdaMinForApply_), std::runtime_error,
                             prefix << "There is no estimate for the min eigenvalue."
                                    << std::endl
                                    << computeBeforeApplyReminder);
  TEUCHOS_TEST_FOR_EXCEPTION(STS::isnaninf(eigRatioForApply_), std::runtime_error,
                             prefix << "There is no estimate for the ratio of the max "
                                       "eigenvalue to the min eigenvalue."
                                    << std::endl
                                    << computeBeforeApplyReminder);
  TEUCHOS_TEST_FOR_EXCEPTION(D_.is_null(), std::runtime_error, prefix << "The vector of inverse "
                                                                         "diagonal entries of the matrix has not yet been computed."
                                                                      << std::endl
                                                                      << computeBeforeApplyReminder);

  if (chebyshevAlgorithm_ == "fourth" || chebyshevAlgorithm_ == "opt_fourth") {
    fourthKindApplyImpl(*A_, B, X, numIters_, lambdaMaxForApply_, *D_);
  } else if (chebyshevAlgorithm_ == "textbook") {
    textbookApplyImpl(*A_, B, X, numIters_, lambdaMaxForApply_,
                      lambdaMinForApply_, eigRatioForApply_, *D_);
  } else {
    ifpackApplyImpl(*A_, B, X, numIters_, lambdaMaxForApply_,
                    lambdaMinForApply_, eigRatioForApply_, *D_);
  }

  if (computeMaxResNorm_ && B.getNumVectors() > 0) {
    MV R(B.getMap(), B.getNumVectors());
    computeResidual(R, B, *A_, X);
    Teuchos::Array<MT> norms(B.getNumVectors());
    R.norm2(norms());
    return *std::max_element(norms.begin(), norms.end());
  } else {
    return Teuchos::ScalarTraits<MT>::zero();
  }
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    print(std::ostream& out) {
  using Teuchos::rcpFromRef;
  this->describe(*(Teuchos::getFancyOStream(rcpFromRef(out))),
                 Teuchos::VERB_MEDIUM);
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    firstIterationWithZeroStartingSolution(MV& W,
                                           const ScalarType& alpha,
                                           const V& D_inv,
                                           const MV& B,
                                           MV& X) {
  solve(W, alpha, D_inv, B);  // W = alpha*D_inv*B
  Tpetra::deep_copy(X, W);    // X = 0 + W
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    computeResidual(MV& R, const MV& B, const op_type& A, const MV& X,
                    const Teuchos::ETransp mode) {
  Tpetra::Details::residual(A, X, B, R);
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    solve(MV& Z, const V& D_inv, const MV& R) {
  Z.elementWiseMultiply(STS::one(), D_inv, R, STS::zero());
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    solve(MV& Z, const ST alpha, const V& D_inv, const MV& R) {
  Z.elementWiseMultiply(alpha, D_inv, R, STS::zero());
}

template <class ScalarType, class MV>
Teuchos::RCP<const typename Chebyshev<ScalarType, MV>::V>
Chebyshev<ScalarType, MV>::
    makeInverseDiagonal(const row_matrix_type& A, const bool useDiagOffsets) const {
  using Teuchos::RCP;
  using Teuchos::rcp_dynamic_cast;
  using Teuchos::rcpFromRef;

  RCP<V> D_rowMap;
  if (!D_.is_null() &&
      D_->getMap()->isSameAs(*(A.getRowMap()))) {
    if (debug_)
      *out_ << "Reusing pre-existing vector for diagonal extraction" << std::endl;
    D_rowMap = Teuchos::rcp_const_cast<V>(D_);
  } else {
    D_rowMap = Teuchos::rcp(new V(A.getRowMap(), /*zeroOut=*/false));
    if (debug_)
      *out_ << "Allocated new vector for diagonal extraction" << std::endl;
  }
  if (useDiagOffsets) {
    // The optimizations below only work if A_ is a Tpetra::CrsMatrix.
    // We'll make our best guess about its type here, since we have no
    // way to get back the original fifth template parameter.
    typedef Tpetra::CrsMatrix<typename MV::scalar_type,
                              typename MV::local_ordinal_type,
                              typename MV::global_ordinal_type,
                              typename MV::node_type>
        crs_matrix_type;
    RCP<const crs_matrix_type> A_crsMat =
        rcp_dynamic_cast<const crs_matrix_type>(rcpFromRef(A));
    if (!A_crsMat.is_null()) {
      TEUCHOS_TEST_FOR_EXCEPTION(
          !savedDiagOffsets_, std::logic_error,
          "Ifpack2::Details::Chebyshev::makeInverseDiagonal: "
          "It is not allowed to call this method with useDiagOffsets=true, "
          "if you have not previously saved offsets of diagonal entries.  "
          "This situation should never arise if this class is used properly.  "
          "Please report this bug to the Ifpack2 developers.");
      A_crsMat->getLocalDiagCopy(*D_rowMap, diagOffsets_);
    }
  } else {
    // This always works for a Tpetra::RowMatrix, even if it is not a
    // Tpetra::CrsMatrix.  We just don't have offsets in this case.
    A.getLocalDiagCopy(*D_rowMap);
  }
  RCP<V> D_rangeMap = makeRangeMapVector(D_rowMap);

  if (debug_) {
    // In debug mode, make sure that all diagonal entries are
    // positive, on all processes.  Note that *out_ only prints on
    // Process 0 of the matrix's communicator.
    bool foundNonpositiveValue = false;
    {
      auto D_lcl    = D_rangeMap->getLocalViewHost(Tpetra::Access::ReadOnly);
      auto D_lcl_1d = Kokkos::subview(D_lcl, Kokkos::ALL(), 0);

      typedef typename MV::impl_scalar_type IST;
      typedef typename MV::local_ordinal_type LO;
      typedef Kokkos::ArithTraits<IST> ATS;
      typedef Kokkos::ArithTraits<typename ATS::mag_type> STM;

      const LO lclNumRows = static_cast<LO>(D_rangeMap->getLocalLength());
      for (LO i = 0; i < lclNumRows; ++i) {
        if (STS::real(D_lcl_1d(i)) <= STM::zero()) {
          foundNonpositiveValue = true;
          break;
        }
      }
    }

    using Teuchos::outArg;
    using Teuchos::REDUCE_MIN;
    using Teuchos::reduceAll;

    const int lclSuccess = foundNonpositiveValue ? 0 : 1;
    int gblSuccess       = lclSuccess;  // to be overwritten
    if (!D_rangeMap->getMap().is_null() && D_rangeMap->getMap()->getComm().is_null()) {
      const Teuchos::Comm<int>& comm = *(D_rangeMap->getMap()->getComm());
      reduceAll<int, int>(comm, REDUCE_MIN, lclSuccess, outArg(gblSuccess));
    }
    if (gblSuccess == 1) {
      *out_ << "makeInverseDiagonal: The matrix's diagonal entries all have "
               "positive real part (this is good for Chebyshev)."
            << std::endl;
    } else {
      *out_ << "makeInverseDiagonal: The matrix's diagonal has at least one "
               "entry with nonpositive real part, on at least one process in the "
               "matrix's communicator.  This is bad for Chebyshev."
            << std::endl;
    }
  }

  // Invert the diagonal entries, replacing entries less (in
  // magnitude) than the user-specified value with that value.
  reciprocal_threshold(*D_rangeMap, minDiagVal_);
  return Teuchos::rcp_const_cast<const V>(D_rangeMap);
}

template <class ScalarType, class MV>
Teuchos::RCP<const typename Chebyshev<ScalarType, MV>::V>
Chebyshev<ScalarType, MV>::
    makeRangeMapVectorConst(const Teuchos::RCP<const V>& D) const {
  using Teuchos::RCP;
  using Teuchos::rcp;
  typedef Tpetra::Export<typename MV::local_ordinal_type,
                         typename MV::global_ordinal_type,
                         typename MV::node_type>
      export_type;
  // This throws logic_error instead of runtime_error, because the
  // methods that call makeRangeMapVector should all have checked
  // whether A_ is null before calling this method.
  TEUCHOS_TEST_FOR_EXCEPTION(
      A_.is_null(), std::logic_error,
      "Ifpack2::Details::Chebyshev::"
      "makeRangeMapVector: The input matrix A is null.  Please call setMatrix() "
      "with a nonnull input matrix before calling this method.  This is probably "
      "a bug in Ifpack2; please report this bug to the Ifpack2 developers.");
  TEUCHOS_TEST_FOR_EXCEPTION(
      D.is_null(), std::logic_error,
      "Ifpack2::Details::Chebyshev::"
      "makeRangeMapVector: The input Vector D is null.  This is probably "
      "a bug in Ifpack2; please report this bug to the Ifpack2 developers.");

  RCP<const map_type> sourceMap = D->getMap();
  RCP<const map_type> rangeMap  = A_->getRangeMap();
  RCP<const map_type> rowMap    = A_->getRowMap();

  if (rangeMap->isSameAs(*sourceMap)) {
    // The given vector's Map is the same as the matrix's range Map.
    // That means we don't need to Export.  This is the fast path.
    return D;
  } else {  // We need to Export.
    RCP<const export_type> exporter;
    // Making an Export object from scratch is expensive enough that
    // it's worth the O(1) global reductions to call isSameAs(), to
    // see if we can avoid that cost.
    if (sourceMap->isSameAs(*rowMap)) {
      // We can reuse the matrix's Export object, if there is one.
      exporter = A_->getGraph()->getExporter();
    } else {  // We have to make a new Export object.
      exporter = rcp(new export_type(sourceMap, rangeMap));
    }

    if (exporter.is_null()) {
      return D;  // Row Map and range Map are the same; no need to Export.
    } else {     // Row Map and range Map are _not_ the same; must Export.
      RCP<V> D_out = rcp(new V(*D, Teuchos::Copy));
      D_out->doExport(*D, *exporter, Tpetra::ADD);
      return Teuchos::rcp_const_cast<const V>(D_out);
    }
  }
}

template <class ScalarType, class MV>
Teuchos::RCP<typename Chebyshev<ScalarType, MV>::V>
Chebyshev<ScalarType, MV>::
    makeRangeMapVector(const Teuchos::RCP<V>& D) const {
  using Teuchos::rcp_const_cast;
  return rcp_const_cast<V>(makeRangeMapVectorConst(rcp_const_cast<V>(D)));
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    textbookApplyImpl(const op_type& A,
                      const MV& B,
                      MV& X,
                      const int numIters,
                      const ST lambdaMax,
                      const ST lambdaMin,
                      const ST eigRatio,
                      const V& D_inv) const {
  (void)lambdaMin;  // Forestall compiler warning.
  const ST myLambdaMin = lambdaMax / eigRatio;

  const ST zero = Teuchos::as<ST>(0);
  const ST one  = Teuchos::as<ST>(1);
  const ST two  = Teuchos::as<ST>(2);
  const ST d    = (lambdaMax + myLambdaMin) / two;  // Ifpack2 calls this theta
  const ST c    = (lambdaMax - myLambdaMin) / two;  // Ifpack2 calls this 1/delta

  if (zeroStartingSolution_ && numIters > 0) {
    // If zero iterations, then input X is output X.
    X.putScalar(zero);
  }
  MV R(B.getMap(), B.getNumVectors(), false);
  MV P(B.getMap(), B.getNumVectors(), false);
  MV Z(B.getMap(), B.getNumVectors(), false);
  ST alpha, beta;
  for (int i = 0; i < numIters; ++i) {
    computeResidual(R, B, A, X);  // R = B - A*X
    solve(Z, D_inv, R);           // z = D_inv * R, that is, D \ R.
    if (i == 0) {
      P     = Z;
      alpha = two / d;
    } else {
      // beta = (c * alpha / two)^2;
      // const ST sqrtBeta = c * alpha / two;
      // beta = sqrtBeta * sqrtBeta;
      beta  = alpha * (c / two) * (c / two);
      alpha = one / (d - beta);
      P.update(one, Z, beta);  // P = Z + beta*P
    }
    X.update(alpha, P, one);  // X = X + alpha*P
    // If we compute the residual here, we could either do R = B -
    // A*X, or R = R - alpha*A*P.  Since we choose the former, we
    // can move the computeResidual call to the top of the loop.
  }
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    fourthKindApplyImpl(const op_type& A,
                        const MV& B,
                        MV& X,
                        const int numIters,
                        const ST lambdaMax,
                        const V& D_inv) {
  // standard 4th kind Chebyshev smoother has \beta_i := 1
  std::vector<ScalarType> betas(numIters, 1.0);
  if (chebyshevAlgorithm_ == "opt_fourth") {
    betas = optimalWeightsImpl<ScalarType>(numIters);
  }

  const ST invEig = MT(1) / (lambdaMax * boostFactor_);

  // Fetch cached temporary (multi)vector.
  Teuchos::RCP<MV> Z_ptr = makeTempMultiVector(B);
  MV& Z                  = *Z_ptr;

  // Store 4th-kind result (needed as temporary for bootstrapping opt. 4th-kind Chebyshev)
  // Fetch the second cached temporary (multi)vector.
  Teuchos::RCP<MV> X4_ptr = makeSecondTempMultiVector(B);
  MV& X4                  = *X4_ptr;

  // Special case for the first iteration.
  if (!zeroStartingSolution_) {
    // X4 = X
    Tpetra::deep_copy(X4, X);

    if (ck_.is_null()) {
      Teuchos::RCP<const op_type> A_op = A_;
      ck_                              = Teuchos::rcp(new ChebyshevKernel<op_type>(A_op, ckUseNativeSpMV_));
    }
    // Z := (4/3 * invEig)*D_inv*(B-A*X4)
    // X4 := X4 + Z
    ck_->compute(Z, MT(4.0 / 3.0) * invEig, const_cast<V&>(D_inv),
                 const_cast<MV&>(B), X4, STS::zero());

    // X := X + beta[0] * Z
    X.update(betas[0], Z, STS::one());
  } else {
    // Z := (4/3 * invEig)*D_inv*B and X := 0 + Z.
    firstIterationWithZeroStartingSolution(Z, MT(4.0 / 3.0) * invEig, D_inv, B, X4);

    // X := 0 + beta * Z
    X.update(betas[0], Z, STS::zero());
  }

  if (numIters > 1 && ck_.is_null()) {
    Teuchos::RCP<const op_type> A_op = A_;
    ck_                              = Teuchos::rcp(new ChebyshevKernel<op_type>(A_op, ckUseNativeSpMV_));
  }

  for (int i = 1; i < numIters; ++i) {
    const ST zScale = (2.0 * i - 1.0) / (2.0 * i + 3.0);
    const ST rScale = MT((8.0 * i + 4.0) / (2.0 * i + 3.0)) * invEig;

    // Z := rScale*D_inv*(B - A*X4) + zScale*Z.
    // X4 := X4 + Z
    ck_->compute(Z, rScale, const_cast<V&>(D_inv),
                 const_cast<MV&>(B), (X4), zScale);

    // X := X + beta[i] * Z
    X.update(betas[i], Z, STS::one());
  }
}

template <class ScalarType, class MV>
typename Chebyshev<ScalarType, MV>::MT
Chebyshev<ScalarType, MV>::maxNormInf(const MV& X) {
  Teuchos::Array<MT> norms(X.getNumVectors());
  X.normInf(norms());
  return *std::max_element(norms.begin(), norms.end());
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    ifpackApplyImpl(const op_type& A,
                    const MV& B,
                    MV& X,
                    const int numIters,
                    const ST lambdaMax,
                    const ST lambdaMin,
                    const ST eigRatio,
                    const V& D_inv) {
  using std::endl;
#ifdef HAVE_IFPACK2_DEBUG
  const bool debug = debug_;
#else
  const bool debug = false;
#endif

  if (debug) {
    *out_ << " \\|B\\|_{\\infty} = " << maxNormInf(B) << endl;
    *out_ << " \\|X\\|_{\\infty} = " << maxNormInf(X) << endl;
  }

  if (numIters <= 0) {
    return;
  }
  const ST zero = static_cast<ST>(0.0);
  const ST one  = static_cast<ST>(1.0);
  const ST two  = static_cast<ST>(2.0);

  // Quick solve when the matrix A is the identity.
  if (lambdaMin == one && lambdaMax == lambdaMin) {
    solve(X, D_inv, B);
    return;
  }

  // Initialize coefficients
  const ST alpha = lambdaMax / eigRatio;
  const ST beta  = boostFactor_ * lambdaMax;
  const ST delta = two / (beta - alpha);
  const ST theta = (beta + alpha) / two;
  const ST s1    = theta * delta;

  if (debug) {
    *out_ << " alpha = " << alpha << endl
          << " beta = " << beta << endl
          << " delta = " << delta << endl
          << " theta = " << theta << endl
          << " s1 = " << s1 << endl;
  }

  // Fetch cached temporary (multi)vector.
  Teuchos::RCP<MV> W_ptr = makeTempMultiVector(B);
  MV& W                  = *W_ptr;

  if (debug) {
    *out_ << " Iteration " << 1 << ":" << endl
          << " - \\|D\\|_{\\infty} = " << D_->normInf() << endl;
  }

  // Special case for the first iteration.
  if (!zeroStartingSolution_) {
    // mfh 22 May 2019: Tests don't actually exercise this path.

    if (ck_.is_null()) {
      Teuchos::RCP<const op_type> A_op = A_;
      ck_                              = Teuchos::rcp(new ChebyshevKernel<op_type>(A_op, ckUseNativeSpMV_));
    }
    // W := (1/theta)*D_inv*(B-A*X) and X := X + W.
    // X := X + W
    ck_->compute(W, one / theta, const_cast<V&>(D_inv),
                 const_cast<MV&>(B), X, zero);
  } else {
    // W := (1/theta)*D_inv*B and X := 0 + W.
    firstIterationWithZeroStartingSolution(W, one / theta, D_inv, B, X);
  }

  if (debug) {
    *out_ << " - \\|W\\|_{\\infty} = " << maxNormInf(W) << endl
          << " - \\|X\\|_{\\infty} = " << maxNormInf(X) << endl;
  }

  if (numIters > 1 && ck_.is_null()) {
    Teuchos::RCP<const op_type> A_op = A_;
    ck_                              = Teuchos::rcp(new ChebyshevKernel<op_type>(A_op, ckUseNativeSpMV_));
  }

  // The rest of the iterations.
  ST rhok = one / s1;
  ST rhokp1, dtemp1, dtemp2;
  for (int deg = 1; deg < numIters; ++deg) {
    if (debug) {
      *out_ << " Iteration " << deg + 1 << ":" << endl
            << " - \\|D\\|_{\\infty} = " << D_->normInf() << endl
            << " - \\|B\\|_{\\infty} = " << maxNormInf(B) << endl
            << " - \\|A\\|_{\\text{frob}} = " << A_->getFrobeniusNorm()
            << endl
            << " - rhok = " << rhok << endl;
    }

    rhokp1 = one / (two * s1 - rhok);
    dtemp1 = rhokp1 * rhok;
    dtemp2 = two * rhokp1 * delta;
    rhok   = rhokp1;

    if (debug) {
      *out_ << " - dtemp1 = " << dtemp1 << endl
            << " - dtemp2 = " << dtemp2 << endl;
    }

    // W := dtemp2*D_inv*(B - A*X) + dtemp1*W.
    // X := X + W
    ck_->compute(W, dtemp2, const_cast<V&>(D_inv),
                 const_cast<MV&>(B), (X), dtemp1);

    if (debug) {
      *out_ << " - \\|W\\|_{\\infty} = " << maxNormInf(W) << endl
            << " - \\|X\\|_{\\infty} = " << maxNormInf(X) << endl;
    }
  }
}

template <class ScalarType, class MV>
typename Chebyshev<ScalarType, MV>::ST
Chebyshev<ScalarType, MV>::
    cgMethodWithInitGuess(const op_type& A,
                          const V& D_inv,
                          const int numIters,
                          V& r) {
  using std::endl;
  using MagnitudeType = typename STS::magnitudeType;
  if (debug_) {
    *out_ << " cgMethodWithInitGuess:" << endl;
  }

  const ST one = STS::one();
  ST beta, betaOld = one, pAp, pApOld = one, alpha, rHz, rHzOld, rHzOld2 = one, lambdaMax;
  // ST lambdaMin;
  Teuchos::ArrayRCP<MagnitudeType> diag, offdiag;
  Teuchos::RCP<V> p, z, Ap;
  diag.resize(numIters);
  offdiag.resize(numIters - 1);

  p  = rcp(new V(A.getRangeMap()));
  z  = rcp(new V(A.getRangeMap()));
  Ap = rcp(new V(A.getRangeMap()));

  // Tpetra::Details::residual (A, x, *b, *r);
  solve(*p, D_inv, r);
  rHz = r.dot(*p);

  for (int iter = 0; iter < numIters; ++iter) {
    if (debug_) {
      *out_ << "  Iteration " << iter << endl;
    }
    A.apply(*p, *Ap);
    pAp   = p->dot(*Ap);
    alpha = rHz / pAp;
    r.update(-alpha, *Ap, one);
    rHzOld = rHz;
    solve(*z, D_inv, r);
    rHz  = r.dot(*z);
    beta = rHz / rHzOld;
    p->update(one, *z, beta);
    if (iter > 0) {
      diag[iter]        = STS::real((betaOld * betaOld * pApOld + pAp) / rHzOld);
      offdiag[iter - 1] = -STS::real((betaOld * pApOld / (sqrt(rHzOld * rHzOld2))));
      if (debug_) {
        *out_ << " diag[" << iter << "]     = " << diag[iter] << endl;
        *out_ << " offdiag[" << iter - 1 << "] = " << offdiag[iter - 1] << endl;
        *out_ << " rHz = " << rHz << endl;
        *out_ << " alpha = " << alpha << endl;
        *out_ << " beta = " << beta << endl;
      }
    } else {
      diag[iter] = STS::real(pAp / rHzOld);
      if (debug_) {
        *out_ << " diag[" << iter << "]     = " << diag[iter] << endl;
        *out_ << " rHz = " << rHz << endl;
        *out_ << " alpha = " << alpha << endl;
        *out_ << " beta = " << beta << endl;
      }
    }
    rHzOld2 = rHzOld;
    betaOld = beta;
    pApOld  = pAp;
  }

  lambdaMax = LapackHelper<ST>::tri_diag_spectral_radius(diag, offdiag);

  return lambdaMax;
}

template <class ScalarType, class MV>
typename Chebyshev<ScalarType, MV>::ST
Chebyshev<ScalarType, MV>::
    cgMethod(const op_type& A, const V& D_inv, const int numIters) {
  using std::endl;

  if (debug_) {
    *out_ << "cgMethod:" << endl;
  }

  Teuchos::RCP<V> r;
  if (eigVector_.is_null()) {
    r = rcp(new V(A.getDomainMap()));
    if (eigKeepVectors_)
      eigVector_ = r;
    // For CG, we need to get the BCs right and we'll use D_inv to get that
    Details::computeInitialGuessForCG(D_inv, *r);
  } else
    r = eigVector_;

  ST lambdaMax = cgMethodWithInitGuess(A, D_inv, numIters, *r);

  return lambdaMax;
}

template <class ScalarType, class MV>
Teuchos::RCP<const typename Chebyshev<ScalarType, MV>::row_matrix_type>
Chebyshev<ScalarType, MV>::getMatrix() const {
  return A_;
}

template <class ScalarType, class MV>
bool Chebyshev<ScalarType, MV>::
    hasTransposeApply() const {
  // Technically, this is true, because the matrix must be symmetric.
  return true;
}

template <class ScalarType, class MV>
Teuchos::RCP<MV>
Chebyshev<ScalarType, MV>::
    makeTempMultiVector(const MV& B) {
  // ETP 02/08/17:  We must check not only if the temporary vectors are
  // null, but also if the number of columns match, since some multi-RHS
  // solvers (e.g., Belos) may call apply() with different numbers of columns.

  const size_t B_numVecs = B.getNumVectors();
  if (W_.is_null() || W_->getNumVectors() != B_numVecs) {
    W_ = Teuchos::rcp(new MV(B.getMap(), B_numVecs, false));
  }
  return W_;
}

template <class ScalarType, class MV>
Teuchos::RCP<MV>
Chebyshev<ScalarType, MV>::
    makeSecondTempMultiVector(const MV& B) {
  // ETP 02/08/17:  We must check not only if the temporary vectors are
  // null, but also if the number of columns match, since some multi-RHS
  // solvers (e.g., Belos) may call apply() with different numbers of columns.

  const size_t B_numVecs = B.getNumVectors();
  if (W2_.is_null() || W2_->getNumVectors() != B_numVecs) {
    W2_ = Teuchos::rcp(new MV(B.getMap(), B_numVecs, false));
  }
  return W2_;
}

template <class ScalarType, class MV>
std::string
Chebyshev<ScalarType, MV>::
    description() const {
  std::ostringstream oss;
  // YAML requires quoting the key in this case, to distinguish
  // key's colons from the colon that separates key from value.
  oss << "\"Ifpack2::Details::Chebyshev\":"
      << "{"
      << "degree: " << numIters_
      << ", lambdaMax: " << lambdaMaxForApply_
      << ", alpha: " << eigRatioForApply_
      << ", lambdaMin: " << lambdaMinForApply_
      << ", boost factor: " << boostFactor_
      << ", algorithm: " << chebyshevAlgorithm_;
  if (!userInvDiag_.is_null())
    oss << ", diagonal: user-supplied";
  oss << "}";
  return oss.str();
}

template <class ScalarType, class MV>
void Chebyshev<ScalarType, MV>::
    describe(Teuchos::FancyOStream& out,
             const Teuchos::EVerbosityLevel verbLevel) const {
  using std::endl;
  using Teuchos::TypeNameTraits;

  const Teuchos::EVerbosityLevel vl =
      (verbLevel == Teuchos::VERB_DEFAULT) ? Teuchos::VERB_LOW : verbLevel;
  if (vl == Teuchos::VERB_NONE) {
    return;  // print NOTHING
  }

  // By convention, describe() starts with a tab.
  //
  // This does affect all processes on which it's valid to print to
  // 'out'.  However, it does not actually print spaces to 'out'
  // unless operator<< gets called, so it's safe to use on all
  // processes.
  Teuchos::OSTab tab0(out);

  // We only print on Process 0 of the matrix's communicator.  If
  // the matrix isn't set, we don't have a communicator, so we have
  // to assume that every process can print.
  int myRank = -1;
  if (A_.is_null() || A_->getComm().is_null()) {
    myRank = 0;
  } else {
    myRank = A_->getComm()->getRank();
  }
  if (myRank == 0) {
    // YAML requires quoting the key in this case, to distinguish
    // key's colons from the colon that separates key from value.
    out << "\"Ifpack2::Details::Chebyshev\":" << endl;
  }
  Teuchos::OSTab tab1(out);

  if (vl == Teuchos::VERB_LOW) {
    if (myRank == 0) {
      out << "degree: " << numIters_ << endl
          << "lambdaMax: " << lambdaMaxForApply_ << endl
          << "alpha: " << eigRatioForApply_ << endl
          << "lambdaMin: " << lambdaMinForApply_ << endl
          << "boost factor: " << boostFactor_ << endl;
    }
    return;
  }

  // vl > Teuchos::VERB_LOW

  if (myRank == 0) {
    out << "Template parameters:" << endl;
    {
      Teuchos::OSTab tab2(out);
      out << "ScalarType: " << TypeNameTraits<ScalarType>::name() << endl
          << "MV: " << TypeNameTraits<MV>::name() << endl;
    }

    // "Computed parameters" literally means "parameters whose
    // values were computed by compute()."
    if (myRank == 0) {
      out << "Computed parameters:" << endl;
    }
  }

  // Print computed parameters
  {
    Teuchos::OSTab tab2(out);
    // Users might want to see the values in the computed inverse
    // diagonal, so we print them out at the highest verbosity.
    if (myRank == 0) {
      out << "D_: ";
    }
    if (D_.is_null()) {
      if (myRank == 0) {
        out << "unset" << endl;
      }
    } else if (vl <= Teuchos::VERB_HIGH) {
      if (myRank == 0) {
        out << "set" << endl;
      }
    } else {  // D_ not null and vl > Teuchos::VERB_HIGH
      if (myRank == 0) {
        out << endl;
      }
      // By convention, describe() first indents, then prints.
      // We can rely on other describe() implementations to do that.
      D_->describe(out, vl);
    }
    if (myRank == 0) {
      // W_ is scratch space; its values are irrelevant.
      // All that matters is whether or not they have been set.
      out << "W_: " << (W_.is_null() ? "unset" : "set") << endl
          << "computedLambdaMax_: " << computedLambdaMax_ << endl
          << "computedLambdaMin_: " << computedLambdaMin_ << endl
          << "lambdaMaxForApply_: " << lambdaMaxForApply_ << endl
          << "lambdaMinForApply_: " << lambdaMinForApply_ << endl
          << "eigRatioForApply_: " << eigRatioForApply_ << endl;
    }
  }  // print computed parameters

  if (myRank == 0) {
    out << "User parameters:" << endl;
  }

  // Print user parameters
  {
    Teuchos::OSTab tab2(out);
    out << "userInvDiag_: ";
    if (userInvDiag_.is_null()) {
      out << "unset" << endl;
    } else if (vl <= Teuchos::VERB_HIGH) {
      out << "set" << endl;
    } else {  // userInvDiag_ not null and vl > Teuchos::VERB_HIGH
      if (myRank == 0) {
        out << endl;
      }
      userInvDiag_->describe(out, vl);
    }
    if (myRank == 0) {
      out << "userLambdaMax_: " << userLambdaMax_ << endl
          << "userLambdaMin_: " << userLambdaMin_ << endl
          << "userEigRatio_: " << userEigRatio_ << endl
          << "numIters_: " << numIters_ << endl
          << "eigMaxIters_: " << eigMaxIters_ << endl
          << "eigRelTolerance_: " << eigRelTolerance_ << endl
          << "eigNormalizationFreq_: " << eigNormalizationFreq_ << endl
          << "zeroStartingSolution_: " << zeroStartingSolution_ << endl
          << "assumeMatrixUnchanged_: " << assumeMatrixUnchanged_ << endl
          << "chebyshevAlgorithm_: " << chebyshevAlgorithm_ << endl
          << "computeMaxResNorm_: " << computeMaxResNorm_ << endl;
    }
  }  // print user parameters
}

}  // namespace Details
}  // namespace Ifpack2

#define IFPACK2_DETAILS_CHEBYSHEV_INSTANT(S, LO, GO, N) \
  template class Ifpack2::Details::Chebyshev<S, Tpetra::MultiVector<S, LO, GO, N>>;

#endif  // IFPACK2_DETAILS_CHEBYSHEV_DEF_HPP