87 lines
3.4 KiB
C++
87 lines
3.4 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 product_selfadjoint(const MatrixType& m)
|
||
|
|
{
|
||
|
|
typedef typename MatrixType::Scalar Scalar;
|
||
|
|
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 = internal::random<Scalar>(),
|
||
|
|
s2 = internal::random<Scalar>(),
|
||
|
|
s3 = internal::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 + (s3*(-v1)*(s2*v2).adjoint()+numext::conj(s3)*(s2*v2)*(-v1).adjoint())).template triangularView<Upper>().toDenseMatrix());
|
||
|
|
|
||
|
|
m2 = m1.template triangularView<Upper>();
|
||
|
|
m2.template selfadjointView<Upper>().rankUpdate(-s2*r1.adjoint(),r2.adjoint()*s3,s1);
|
||
|
|
VERIFY_IS_APPROX(m2, (m1 + s1*(-s2*r1.adjoint())*(r2.adjoint()*s3).adjoint() + numext::conj(s1)*(r2.adjoint()*s3) * (-s2*r1.adjoint()).adjoint()).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());
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
EIGEN_DECLARE_TEST(product_selfadjoint)
|
||
|
|
{
|
||
|
|
int s = 0;
|
||
|
|
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 = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
|
||
|
|
CALL_SUBTEST_4( product_selfadjoint(MatrixXcf(s, s)) );
|
||
|
|
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||
|
|
|
||
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
|
||
|
|
CALL_SUBTEST_5( product_selfadjoint(MatrixXcd(s,s)) );
|
||
|
|
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||
|
|
|
||
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
|
||
|
|
CALL_SUBTEST_6( product_selfadjoint(MatrixXd(s,s)) );
|
||
|
|
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||
|
|
|
||
|
|
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
|
||
|
|
CALL_SUBTEST_7( product_selfadjoint(Matrix<float,Dynamic,Dynamic,RowMajor>(s,s)) );
|
||
|
|
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||
|
|
}
|
||
|
|
}
|