chromium/third_party/eigen3/src/Eigen/src/Core/StableNorm.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <[email protected]>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_STABLENORM_H
#define EIGEN_STABLENORM_H

// IWYU pragma: private
#include "./InternalHeaderCheck.h"

namespace Eigen {

namespace internal {

template <typename ExpressionType, typename Scalar>
inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale) { … }

template <typename VectorType, typename RealScalar>
void stable_norm_impl_inner_step(const VectorType& vec, RealScalar& ssq, RealScalar& scale, RealScalar& invScale) { … }

template <typename VectorType>
typename VectorType::RealScalar stable_norm_impl(const VectorType& vec,
                                                 std::enable_if_t<VectorType::IsVectorAtCompileTime>* = 0) { … }

template <typename MatrixType>
typename MatrixType::RealScalar stable_norm_impl(const MatrixType& mat,
                                                 std::enable_if_t<!MatrixType::IsVectorAtCompileTime>* = 0) { … }

template <typename Derived>
inline typename NumTraits<typename traits<Derived>::Scalar>::Real blueNorm_impl(const EigenBase<Derived>& _vec) { … }

}  // end namespace internal

/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
 * This version use a blockwise two passes algorithm:
 *  1 - find the absolute largest coefficient \c s
 *  2 - compute \f$ s \Vert \frac{*this}{s} \Vert \f$ in a standard way
 *
 * For architecture/scalar types supporting vectorization, this version
 * is faster than blueNorm(). Otherwise the blueNorm() is much faster.
 *
 * \sa norm(), blueNorm(), hypotNorm()
 */
template <typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::stableNorm() const { … }

/** \returns the \em l2 norm of \c *this using the Blue's algorithm.
 * A Portable Fortran Program to Find the Euclidean Norm of a Vector,
 * ACM TOMS, Vol 4, Issue 1, 1978.
 *
 * For architecture/scalar types without vectorization, this version
 * is much faster than stableNorm(). Otherwise the stableNorm() is faster.
 *
 * \sa norm(), stableNorm(), hypotNorm()
 */
template <typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::blueNorm() const { … }

/** \returns the \em l2 norm of \c *this avoiding undeflow and overflow.
 * This version use a concatenation of hypot() calls, and it is very slow.
 *
 * \sa norm(), stableNorm()
 */
template <typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::hypotNorm() const { … }

}  // end namespace Eigen

#endif  // EIGEN_STABLENORM_H