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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#include "main.h"
template<typename MatrixType> void product_selfadjoint(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> RowVectorType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, Dynamic, RowMajor> RhsMatrixType;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3;
VectorType v1 = VectorType::Random(rows),
v2 = VectorType::Random(rows),
v3(rows);
RowVectorType r1 = RowVectorType::Random(rows),
r2 = RowVectorType::Random(rows);
RhsMatrixType m4 = RhsMatrixType::Random(rows,10);
Scalar s1 = ei_random<Scalar>(),
s2 = ei_random<Scalar>(),
s3 = ei_random<Scalar>();
m1 = (m1.adjoint() + m1).eval();
// rank2 update
m2 = m1.template triangularView<Lower>();
m2.template selfadjointView<Lower>().rankUpdate(v1,v2);
VERIFY_IS_APPROX(m2, (m1 + v1 * v2.adjoint()+ v2 * v1.adjoint()).template triangularView<Lower>().toDenseMatrix());
m2 = m1.template triangularView<Upper>();
m2.template selfadjointView<Upper>().rankUpdate(-v1,s2*v2,s3);
VERIFY_IS_APPROX(m2, (m1 + (-s2*s3) * (v1 * v2.adjoint()+ v2 * v1.adjoint())).template triangularView<Upper>().toDenseMatrix());
m2 = m1.template triangularView<Upper>();
m2.template selfadjointView<Upper>().rankUpdate(-r1.adjoint(),r2.adjoint()*s3,s1);
VERIFY_IS_APPROX(m2, (m1 + (-s3*s1) * (r1.adjoint() * r2 + r2.adjoint() * r1)).template triangularView<Upper>().toDenseMatrix());
if (rows>1)
{
m2 = m1.template triangularView<Lower>();
m2.block(1,1,rows-1,cols-1).template selfadjointView<Lower>().rankUpdate(v1.tail(rows-1),v2.head(cols-1));
m3 = m1;
m3.block(1,1,rows-1,cols-1) += v1.tail(rows-1) * v2.head(cols-1).adjoint()+ v2.head(cols-1) * v1.tail(rows-1).adjoint();
VERIFY_IS_APPROX(m2, m3.template triangularView<Lower>().toDenseMatrix());
}
}
void test_product_selfadjoint()
{
int s;
for(int i = 0; i < g_repeat ; i++) {
CALL_SUBTEST_1( product_selfadjoint(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( product_selfadjoint(Matrix<float, 2, 2>()) );
CALL_SUBTEST_3( product_selfadjoint(Matrix3d()) );
s = ei_random<int>(1,150);
CALL_SUBTEST_4( product_selfadjoint(MatrixXcf(s, s)) );
s = ei_random<int>(1,150);
CALL_SUBTEST_5( product_selfadjoint(MatrixXcd(s,s)) );
s = ei_random<int>(1,320);
CALL_SUBTEST_6( product_selfadjoint(MatrixXd(s,s)) );
s = ei_random<int>(1,320);
CALL_SUBTEST_7( product_selfadjoint(Matrix<float,Dynamic,Dynamic,RowMajor>(s,s)) );
}
}
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