diff options
| -rw-r--r-- | Eigen/src/SparseQR/SparseQR.h | 16 | ||||
| -rw-r--r-- | test/sparseqr.cpp | 2 |
2 files changed, 16 insertions, 2 deletions
diff --git a/Eigen/src/SparseQR/SparseQR.h b/Eigen/src/SparseQR/SparseQR.h index 66dccfc43..002b4824b 100644 --- a/Eigen/src/SparseQR/SparseQR.h +++ b/Eigen/src/SparseQR/SparseQR.h @@ -75,7 +75,7 @@ class SparseQR typedef Matrix<Scalar, Dynamic, 1> ScalarVector; typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; public: - SparseQR () : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false) + SparseQR () : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false) { } /** Construct a QR factorization of the matrix \a mat. @@ -84,7 +84,7 @@ class SparseQR * * \sa compute() */ - SparseQR(const MatrixType& mat) : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false) + SparseQR(const MatrixType& mat) : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false) { compute(mat); } @@ -262,6 +262,7 @@ class SparseQR IndexVector m_etree; // Column elimination tree IndexVector m_firstRowElt; // First element in each row bool m_isQSorted; // whether Q is sorted or not + bool m_isEtreeOk; // whether the elimination tree match the initial input matrix template <typename, typename > friend struct SparseQR_QProduct; template <typename > friend struct SparseQRMatrixQReturnType; @@ -297,6 +298,7 @@ void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat) // Compute the column elimination tree of the permuted matrix m_outputPerm_c = m_perm_c.inverse(); internal::coletree(mat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data()); + m_isEtreeOk = true; m_R.resize(m, n); m_Q.resize(m, diagSize); @@ -330,6 +332,15 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) Index nzcolR, nzcolQ; // Number of nonzero for the current column of R and Q ScalarVector tval(m); // The dense vector used to compute the current column RealScalar pivotThreshold = m_threshold; + + m_R.setZero(); + m_Q.setZero(); + if(!m_isEtreeOk) + { + m_outputPerm_c = m_perm_c.inverse(); + internal::coletree(mat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data()); + m_isEtreeOk = true; + } m_pmat = mat; m_pmat.uncompress(); // To have the innerNonZeroPtr allocated @@ -513,6 +524,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) // Recompute the column elimination tree internal::coletree(m_pmat, m_etree, m_firstRowElt, m_pivotperm.indices().data()); + m_isEtreeOk = false; } } diff --git a/test/sparseqr.cpp b/test/sparseqr.cpp index 1fe4a98ee..8e6887dd3 100644 --- a/test/sparseqr.cpp +++ b/test/sparseqr.cpp @@ -54,6 +54,8 @@ template<typename Scalar> void test_sparseqr_scalar() b = dA * DenseVector::Random(A.cols()); solver.compute(A); + if(internal::random<float>(0,1)>0.5) + solver.factorize(A); // this checks that calling analyzePattern is not needed if the pattern do not change. if (solver.info() != Success) { std::cerr << "sparse QR factorization failed\n"; |