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Further LU test improvements. I'm not aware of any test failures anymore, not even with huge numbers of repetitions.

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Finally the createRandomMatrixOfRank() function is renamed to createRandomPIMatrixOfRank, where PI stands for 'partial isometry', that is, a matrix whose singular values are 0 or 1.
This commit is contained in:
Benoit Jacob
2010-02-23 15:40:24 -05:00
parent 7dc75380c1
commit d92df336ad
6 changed files with 42 additions and 50 deletions
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12
test/main.h
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@@ -148,7 +148,7 @@ namespace Eigen
#define EIGEN_INTERNAL_DEBUGGING
#define EIGEN_NICE_RANDOM
#include <Eigen/QR> // required for createRandomProjectionOfRank
#include <Eigen/QR> // required for createRandomPIMatrixOfRank
#define VERIFY(a) do { if (!(a)) { \
@@ -342,8 +342,13 @@ inline bool test_isUnitary(const MatrixBase<Derived>& m)
return m.isUnitary(test_precision<typename ei_traits<Derived>::Scalar>());
}
/** Creates a random Partial Isometry matrix of given rank.
*
* A partial isometry is a matrix all of whose singular values are either 0 or 1.
* This is very useful to test rank-revealing algorithms.
*/
template<typename MatrixType>
void createRandomProjectionOfRank(int desired_rank, int rows, int cols, MatrixType& m)
void createRandomPIMatrixOfRank(int desired_rank, int rows, int cols, MatrixType& m)
{
typedef typename ei_traits<MatrixType>::Scalar Scalar;
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
@@ -360,7 +365,8 @@ void createRandomProjectionOfRank(int desired_rank, int rows, int cols, MatrixTy
if(desired_rank == 1)
{
m = VectorType::Random(rows) * VectorType::Random(cols).transpose();
// here we normalize the vectors to get a partial isometry
m = VectorType::Random(rows).normalized() * VectorType::Random(cols).normalized().transpose();
return;
}