diff options
Diffstat (limited to 'Eigen/src/QR/ColPivHouseholderQR.h')
| -rw-r--r-- | Eigen/src/QR/ColPivHouseholderQR.h | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/Eigen/src/QR/ColPivHouseholderQR.h b/Eigen/src/QR/ColPivHouseholderQR.h index 1b2472a99..3e33fadbf 100644 --- a/Eigen/src/QR/ColPivHouseholderQR.h +++ b/Eigen/src/QR/ColPivHouseholderQR.h @@ -359,14 +359,14 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const { // first, we look up in our table colSqNorms which column has the biggest squared norm int biggest_col_index; - RealScalar biggest_col_sq_norm = colSqNorms.end(cols-k).maxCoeff(&biggest_col_index); + RealScalar biggest_col_sq_norm = colSqNorms.tail(cols-k).maxCoeff(&biggest_col_index); biggest_col_index += k; // since our table colSqNorms accumulates imprecision at every step, we must now recompute // the actual squared norm of the selected column. // Note that not doing so does result in solve() sometimes returning inf/nan values // when running the unit test with 1000 repetitions. - biggest_col_sq_norm = m_qr.col(biggest_col_index).end(rows-k).squaredNorm(); + biggest_col_sq_norm = m_qr.col(biggest_col_index).tail(rows-k).squaredNorm(); // we store that back into our table: it can't hurt to correct our table. colSqNorms.coeffRef(biggest_col_index) = biggest_col_sq_norm; @@ -379,7 +379,7 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const if(biggest_col_sq_norm < threshold_helper * (rows-k)) { m_nonzero_pivots = k; - m_hCoeffs.end(size-k).setZero(); + m_hCoeffs.tail(size-k).setZero(); m_qr.corner(BottomRight,rows-k,cols-k) .template triangularView<StrictlyLowerTriangular>() .setZero(); @@ -396,7 +396,7 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const // generate the householder vector, store it below the diagonal RealScalar beta; - m_qr.col(k).end(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta); + m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta); // apply the householder transformation to the diagonal coefficient m_qr.coeffRef(k,k) = beta; @@ -406,10 +406,10 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const // apply the householder transformation m_qr.corner(BottomRight, rows-k, cols-k-1) - .applyHouseholderOnTheLeft(m_qr.col(k).end(rows-k-1), m_hCoeffs.coeffRef(k), &temp.coeffRef(k+1)); + .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &temp.coeffRef(k+1)); // update our table of squared norms of the columns - colSqNorms.end(cols-k-1) -= m_qr.row(k).end(cols-k-1).cwise().abs2(); + colSqNorms.tail(cols-k-1) -= m_qr.row(k).tail(cols-k-1).cwise().abs2(); } m_cols_permutation.setIdentity(cols); |