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* add a HouseholderSequence class (not good enough yet for Triadiagonalization and HessenbergDecomposition)

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* rework a bit AnyMatrixBase, and mobe it to a separate file
This commit is contained in:
Gael Guennebaud
2009-09-16 14:35:42 +02:00
parent 77f858f6ab
commit 49dd5d7847
12 changed files with 339 additions and 103 deletions
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9
test/householder.cpp
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@@ -43,7 +43,7 @@ template<typename MatrixType> void householder(const MatrixType& m)
Matrix<Scalar, EIGEN_ENUM_MAX(MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime), 1> _tmp(std::max(rows,cols));
Scalar* tmp = &_tmp.coeffRef(0,0);
Scalar beta;
RealScalar alpha;
EssentialVectorType essential;
@@ -58,7 +58,7 @@ template<typename MatrixType> void householder(const MatrixType& m)
v2 = v1;
v1.applyHouseholderOnTheLeft(essential,beta,tmp);
VERIFY_IS_APPROX(v1.norm(), v2.norm());
MatrixType m1(rows, cols),
m2(rows, cols);
@@ -72,7 +72,7 @@ template<typename MatrixType> void householder(const MatrixType& m)
VERIFY_IS_MUCH_SMALLER_THAN(m1.block(1,0,rows-1,cols).norm(), m1.norm());
VERIFY_IS_MUCH_SMALLER_THAN(ei_imag(m1(0,0)), ei_real(m1(0,0)));
VERIFY_IS_APPROX(ei_real(m1(0,0)), alpha);
v1 = VectorType::Random(rows);
if(even) v1.end(rows-1).setZero();
SquareMatrixType m3(rows,rows), m4(rows,rows);
@@ -84,6 +84,9 @@ template<typename MatrixType> void householder(const MatrixType& m)
VERIFY_IS_MUCH_SMALLER_THAN(m3.block(0,1,rows,rows-1).norm(), m3.norm());
VERIFY_IS_MUCH_SMALLER_THAN(ei_imag(m3(0,0)), ei_real(m3(0,0)));
VERIFY_IS_APPROX(ei_real(m3(0,0)), alpha);
// test householder sequence
// TODO test HouseholderSequence
}
void test_householder()

8
test/jacobisvd.cpp
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@@ -36,14 +36,14 @@ template<typename MatrixType, unsigned int Options> void svd(const MatrixType& m
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType;
typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType;
typedef Matrix<Scalar, RowsAtCompileTime, 1> ColVectorType;
typedef Matrix<Scalar, ColsAtCompileTime, 1> InputVectorType;
MatrixType a;
if(pickrandom) a = MatrixType::Random(rows,cols);
else a = m;
@@ -53,7 +53,7 @@ template<typename MatrixType, unsigned int Options> void svd(const MatrixType& m
sigma.diagonal() = svd.singularValues().template cast<Scalar>();
MatrixUType u = svd.matrixU();
MatrixVType v = svd.matrixV();
VERIFY_IS_APPROX(a, u * sigma * v.adjoint());
VERIFY_IS_UNITARY(u);
VERIFY_IS_UNITARY(v);
@@ -98,7 +98,7 @@ void test_jacobisvd()
}
CALL_SUBTEST(( svd<MatrixXf,0>(MatrixXf(300,200)) ));
CALL_SUBTEST(( svd<MatrixXcd,AtLeastAsManyColsAsRows>(MatrixXcd(100,150)) ));
CALL_SUBTEST(( svd_verify_assert<Matrix3f>() ));
CALL_SUBTEST(( svd_verify_assert<Matrix3d>() ));
CALL_SUBTEST(( svd_verify_assert<MatrixXf>() ));

2
test/qr.cpp
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@@ -78,7 +78,7 @@ template<typename MatrixType> void qr_invertible()
m3 = MatrixType::Random(size,size);
qr.solve(m3, &m2);
VERIFY_IS_APPROX(m3, m1*m2);
// now construct a matrix with prescribed determinant
m1.setZero();
for(int i = 0; i < size; i++) m1(i,i) = ei_random<Scalar>();