mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-27 07:29:52 +08:00
99 lines
4.5 KiB
C++
99 lines
4.5 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
|
|
//
|
|
// 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/.
|
|
|
|
#include "main.h"
|
|
|
|
template<typename MatrixType> void syrk(const MatrixType& m)
|
|
{
|
|
typedef typename MatrixType::Index Index;
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef typename NumTraits<Scalar>::Real RealScalar;
|
|
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic> Rhs1;
|
|
typedef Matrix<Scalar, Dynamic, MatrixType::RowsAtCompileTime> Rhs2;
|
|
typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, Dynamic,RowMajor> Rhs3;
|
|
|
|
Index rows = m.rows();
|
|
Index cols = m.cols();
|
|
|
|
MatrixType m1 = MatrixType::Random(rows, cols),
|
|
m2 = MatrixType::Random(rows, cols);
|
|
|
|
Rhs1 rhs1 = Rhs1::Random(internal::random<int>(1,320), cols);
|
|
Rhs2 rhs2 = Rhs2::Random(rows, internal::random<int>(1,320));
|
|
Rhs3 rhs3 = Rhs3::Random(internal::random<int>(1,320), rows);
|
|
|
|
Scalar s1 = internal::random<Scalar>();
|
|
|
|
Index c = internal::random<Index>(0,cols-1);
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(rhs2,s1)._expression()),
|
|
((s1 * rhs2 * rhs2.adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs2,s1)._expression(),
|
|
(s1 * rhs2 * rhs2.adjoint()).eval().template triangularView<Upper>().toDenseMatrix());
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX(m2.template selfadjointView<Lower>().rankUpdate(rhs1.adjoint(),s1)._expression(),
|
|
(s1 * rhs1.adjoint() * rhs1).eval().template triangularView<Lower>().toDenseMatrix());
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs1.adjoint(),s1)._expression(),
|
|
(s1 * rhs1.adjoint() * rhs1).eval().template triangularView<Upper>().toDenseMatrix());
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX(m2.template selfadjointView<Lower>().rankUpdate(rhs3.adjoint(),s1)._expression(),
|
|
(s1 * rhs3.adjoint() * rhs3).eval().template triangularView<Lower>().toDenseMatrix());
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX(m2.template selfadjointView<Upper>().rankUpdate(rhs3.adjoint(),s1)._expression(),
|
|
(s1 * rhs3.adjoint() * rhs3).eval().template triangularView<Upper>().toDenseMatrix());
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.col(c),s1)._expression()),
|
|
((s1 * m1.col(c) * m1.col(c).adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.col(c),s1)._expression()),
|
|
((s1 * m1.col(c) * m1.col(c).adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.col(c).conjugate(),s1)._expression()),
|
|
((s1 * m1.col(c).conjugate() * m1.col(c).conjugate().adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.col(c).conjugate(),s1)._expression()),
|
|
((s1 * m1.col(c).conjugate() * m1.col(c).conjugate().adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX((m2.template selfadjointView<Lower>().rankUpdate(m1.row(c),s1)._expression()),
|
|
((s1 * m1.row(c).transpose() * m1.row(c).transpose().adjoint()).eval().template triangularView<Lower>().toDenseMatrix()));
|
|
|
|
m2.setZero();
|
|
VERIFY_IS_APPROX((m2.template selfadjointView<Upper>().rankUpdate(m1.row(c).adjoint(),s1)._expression()),
|
|
((s1 * m1.row(c).adjoint() * m1.row(c).adjoint().adjoint()).eval().template triangularView<Upper>().toDenseMatrix()));
|
|
}
|
|
|
|
void test_product_syrk()
|
|
{
|
|
for(int i = 0; i < g_repeat ; i++)
|
|
{
|
|
int s;
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
|
|
CALL_SUBTEST_1( syrk(MatrixXf(s, s)) );
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
|
|
CALL_SUBTEST_2( syrk(MatrixXd(s, s)) );
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
|
|
CALL_SUBTEST_3( syrk(MatrixXcf(s, s)) );
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
|
|
CALL_SUBTEST_4( syrk(MatrixXcd(s, s)) );
|
|
}
|
|
}
|