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PR 567: makes all dense solvers inherit SoverBase (LU,Cholesky,QR,SVD).

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This changeset also includes:
 * add HouseholderSequence::conjugateIf
 * define int as the StorageIndex type for all dense solvers
 * dedicated unit tests, including assertion checking
 * _check_solve_assertion(): this method can be implemented in derived solver classes to implement custom checks
 * CompleteOrthogonalDecompositions: add applyZOnTheLeftInPlace, fix scalar type in applyZAdjointOnTheLeftInPlace(), add missing assertions
 * Cholesky: add missing assertions
 * FullPivHouseholderQR: Corrected Scalar type in _solve_impl()
 * BDCSVD: Unambiguous return type for ternary operator
 * SVDBase: Corrected Scalar type in _solve_impl()
This commit is contained in:
Patrick Peltzer
2019-01-17 01:17:39 +01:00
parent 7f32109c11
commit 15e53d5d93
23 changed files with 576 additions and 239 deletions
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66
test/lu.cpp
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@@ -9,6 +9,7 @@
#include "main.h"
#include <Eigen/LU>
#include "solverbase.h"
using namespace std;
template<typename MatrixType>
@@ -96,32 +97,14 @@ template<typename MatrixType> void lu_non_invertible()
VERIFY(m1image.fullPivLu().rank() == rank);
VERIFY_IS_APPROX(m1 * m1.adjoint() * m1image, m1image);
check_solverbase<CMatrixType, MatrixType>(m1, lu, rows, cols, cols2);
m2 = CMatrixType::Random(cols,cols2);
m3 = m1*m2;
m2 = CMatrixType::Random(cols,cols2);
// test that the code, which does resize(), may be applied to an xpr
m2.block(0,0,m2.rows(),m2.cols()) = lu.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
// test solve with transposed
m3 = MatrixType::Random(rows,cols2);
m2 = m1.transpose()*m3;
m3 = MatrixType::Random(rows,cols2);
lu.template _solve_impl_transposed<false>(m2, m3);
VERIFY_IS_APPROX(m2, m1.transpose()*m3);
m3 = MatrixType::Random(rows,cols2);
m3 = lu.transpose().solve(m2);
VERIFY_IS_APPROX(m2, m1.transpose()*m3);
// test solve with conjugate transposed
m3 = MatrixType::Random(rows,cols2);
m2 = m1.adjoint()*m3;
m3 = MatrixType::Random(rows,cols2);
lu.template _solve_impl_transposed<true>(m2, m3);
VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
m3 = MatrixType::Random(rows,cols2);
m3 = lu.adjoint().solve(m2);
VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
}
template<typename MatrixType> void lu_invertible()
@@ -150,10 +133,12 @@ template<typename MatrixType> void lu_invertible()
VERIFY(lu.isSurjective());
VERIFY(lu.isInvertible());
VERIFY(lu.image(m1).fullPivLu().isInvertible());
check_solverbase<MatrixType, MatrixType>(m1, lu, size, size, size);
MatrixType m1_inverse = lu.inverse();
m3 = MatrixType::Random(size,size);
m2 = lu.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
MatrixType m1_inverse = lu.inverse();
VERIFY_IS_APPROX(m2, m1_inverse*m3);
RealScalar rcond = (RealScalar(1) / matrix_l1_norm(m1)) / matrix_l1_norm(m1_inverse);
@@ -162,20 +147,6 @@ template<typename MatrixType> void lu_invertible()
// truth.
VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
// test solve with transposed
lu.template _solve_impl_transposed<false>(m3, m2);
VERIFY_IS_APPROX(m3, m1.transpose()*m2);
m3 = MatrixType::Random(size,size);
m3 = lu.transpose().solve(m2);
VERIFY_IS_APPROX(m2, m1.transpose()*m3);
// test solve with conjugate transposed
lu.template _solve_impl_transposed<true>(m3, m2);
VERIFY_IS_APPROX(m3, m1.adjoint()*m2);
m3 = MatrixType::Random(size,size);
m3 = lu.adjoint().solve(m2);
VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
// Regression test for Bug 302
MatrixType m4 = MatrixType::Random(size,size);
VERIFY_IS_APPROX(lu.solve(m3*m4), lu.solve(m3)*m4);
@@ -197,30 +168,17 @@ template<typename MatrixType> void lu_partial_piv()
VERIFY_IS_APPROX(m1, plu.reconstructedMatrix());
check_solverbase<MatrixType, MatrixType>(m1, plu, size, size, size);
MatrixType m1_inverse = plu.inverse();
m3 = MatrixType::Random(size,size);
m2 = plu.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
MatrixType m1_inverse = plu.inverse();
VERIFY_IS_APPROX(m2, m1_inverse*m3);
RealScalar rcond = (RealScalar(1) / matrix_l1_norm(m1)) / matrix_l1_norm(m1_inverse);
const RealScalar rcond_est = plu.rcond();
// Verify that the estimate is within a factor of 10 of the truth.
VERIFY(rcond_est > rcond / 10 && rcond_est < rcond * 10);
// test solve with transposed
plu.template _solve_impl_transposed<false>(m3, m2);
VERIFY_IS_APPROX(m3, m1.transpose()*m2);
m3 = MatrixType::Random(size,size);
m3 = plu.transpose().solve(m2);
VERIFY_IS_APPROX(m2, m1.transpose()*m3);
// test solve with conjugate transposed
plu.template _solve_impl_transposed<true>(m3, m2);
VERIFY_IS_APPROX(m3, m1.adjoint()*m2);
m3 = MatrixType::Random(size,size);
m3 = plu.adjoint().solve(m2);
VERIFY_IS_APPROX(m2, m1.adjoint()*m3);
}
template<typename MatrixType> void lu_verify_assert()
@@ -234,6 +192,8 @@ template<typename MatrixType> void lu_verify_assert()
VERIFY_RAISES_ASSERT(lu.kernel())
VERIFY_RAISES_ASSERT(lu.image(tmp))
VERIFY_RAISES_ASSERT(lu.solve(tmp))
VERIFY_RAISES_ASSERT(lu.transpose().solve(tmp))
VERIFY_RAISES_ASSERT(lu.adjoint().solve(tmp))
VERIFY_RAISES_ASSERT(lu.determinant())
VERIFY_RAISES_ASSERT(lu.rank())
VERIFY_RAISES_ASSERT(lu.dimensionOfKernel())
@@ -246,6 +206,8 @@ template<typename MatrixType> void lu_verify_assert()
VERIFY_RAISES_ASSERT(plu.matrixLU())
VERIFY_RAISES_ASSERT(plu.permutationP())
VERIFY_RAISES_ASSERT(plu.solve(tmp))
VERIFY_RAISES_ASSERT(plu.transpose().solve(tmp))
VERIFY_RAISES_ASSERT(plu.adjoint().solve(tmp))
VERIFY_RAISES_ASSERT(plu.determinant())
VERIFY_RAISES_ASSERT(plu.inverse())
}