100 lines
3.4 KiB
C++
100 lines
3.4 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_SPARSE_DOT_H
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#define EIGEN_SPARSE_DOT_H
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// IWYU pragma: private
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#include "./InternalHeaderCheck.h"
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namespace Eigen {
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template <typename Derived>
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template <typename OtherDerived>
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inline typename internal::traits<Derived>::Scalar SparseMatrixBase<Derived>::dot(
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const MatrixBase<OtherDerived>& other) const {
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EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
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EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
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EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived, OtherDerived)
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EIGEN_STATIC_ASSERT(
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(internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
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YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
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eigen_assert(size() == other.size());
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eigen_assert(other.size() > 0 && "you are using a non initialized vector");
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internal::evaluator<Derived> thisEval(derived());
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typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
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// Two accumulators, which breaks the dependency chain on the accumulator
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// and allows more instruction-level parallelism in the following loop.
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Scalar res1(0);
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Scalar res2(0);
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for (; i; ++i) {
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res1 += numext::conj(i.value()) * other.coeff(i.index());
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++i;
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if (i) {
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res2 += numext::conj(i.value()) * other.coeff(i.index());
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}
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}
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return res1 + res2;
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}
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template <typename Derived>
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template <typename OtherDerived>
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inline typename internal::traits<Derived>::Scalar SparseMatrixBase<Derived>::dot(
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const SparseMatrixBase<OtherDerived>& other) const {
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EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
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EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
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EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived, OtherDerived)
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EIGEN_STATIC_ASSERT(
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(internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
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YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
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eigen_assert(size() == other.size());
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internal::evaluator<Derived> thisEval(derived());
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typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
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internal::evaluator<OtherDerived> otherEval(other.derived());
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typename internal::evaluator<OtherDerived>::InnerIterator j(otherEval, 0);
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Scalar res(0);
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while (i && j) {
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if (i.index() == j.index()) {
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res += numext::conj(i.value()) * j.value();
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++i;
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++j;
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} else if (i.index() < j.index())
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++i;
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else
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++j;
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}
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return res;
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}
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template <typename Derived>
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inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real SparseMatrixBase<Derived>::squaredNorm()
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const {
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return numext::real((*this).cwiseAbs2().sum());
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}
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template <typename Derived>
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inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real SparseMatrixBase<Derived>::norm() const {
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using std::sqrt;
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return sqrt(squaredNorm());
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}
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template <typename Derived>
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inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real SparseMatrixBase<Derived>::blueNorm()
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const {
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return internal::blueNorm_impl(*this);
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}
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} // end namespace Eigen
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#endif // EIGEN_SPARSE_DOT_H
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