diff options
Diffstat (limited to 'include/ceres/covariance.h')
| -rw-r--r-- | include/ceres/covariance.h | 82 |
1 files changed, 22 insertions, 60 deletions
diff --git a/include/ceres/covariance.h b/include/ceres/covariance.h index 83126b5..35fde4d 100644 --- a/include/ceres/covariance.h +++ b/include/ceres/covariance.h @@ -36,6 +36,7 @@ #include "ceres/internal/port.h" #include "ceres/internal/scoped_ptr.h" #include "ceres/types.h" +#include "ceres/internal/disable_warnings.h" namespace ceres { @@ -196,14 +197,14 @@ class CovarianceImpl; // covariance.GetCovarianceBlock(y, y, covariance_yy) // covariance.GetCovarianceBlock(x, y, covariance_xy) // -class Covariance { +class CERES_EXPORT Covariance { public: - struct Options { + struct CERES_EXPORT Options { Options() #ifndef CERES_NO_SUITESPARSE - : algorithm_type(SPARSE_QR), + : algorithm_type(SUITE_SPARSE_QR), #else - : algorithm_type(DENSE_SVD), + : algorithm_type(EIGEN_SPARSE_QR), #endif min_reciprocal_condition_number(1e-14), null_space_rank(0), @@ -228,47 +229,22 @@ class Covariance { // for small to moderate sized problems. It can handle // full-rank as well as rank deficient Jacobians. // - // 2. SPARSE_CHOLESKY uses the CHOLMOD sparse Cholesky - // factorization library to compute the decomposition : - // - // R'R = J'J - // - // and then - // - // [J'J]^-1 = [R'R]^-1 - // - // It a fast algorithm for sparse matrices that should be used - // when the Jacobian matrix J is well conditioned. For - // ill-conditioned matrices, this algorithm can fail - // unpredictabily. This is because Cholesky factorization is - // not a rank-revealing factorization, i.e., it cannot reliably - // detect when the matrix being factorized is not of full - // rank. SuiteSparse/CHOLMOD supplies a heuristic for checking - // if the matrix is rank deficient (cholmod_rcond), but it is - // only a heuristic and can have both false positive and false - // negatives. - // - // Recent versions of SuiteSparse (>= 4.2.0) provide a much - // more efficient method for solving for rows of the covariance - // matrix. Therefore, if you are doing SPARSE_CHOLESKY, we - // strongly recommend using a recent version of SuiteSparse. - // - // 3. SPARSE_QR uses the SuiteSparseQR sparse QR factorization - // library to compute the decomposition + // 2. EIGEN_SPARSE_QR uses the sparse QR factorization algorithm + // in Eigen to compute the decomposition // // Q * R = J // // [J'J]^-1 = [R*R']^-1 // - // It is a moderately fast algorithm for sparse matrices, which - // at the price of more time and memory than the - // SPARSE_CHOLESKY algorithm is numerically better behaved and - // is rank revealing, i.e., it can reliably detect when the - // Jacobian matrix is rank deficient. + // It is a moderately fast algorithm for sparse matrices. // - // Neither SPARSE_CHOLESKY or SPARSE_QR are capable of computing - // the covariance if the Jacobian is rank deficient. - + // 3. SUITE_SPARSE_QR uses the SuiteSparseQR sparse QR + // factorization algorithm. It uses dense linear algebra and is + // multi threaded, so for large sparse sparse matrices it is + // significantly faster than EIGEN_SPARSE_QR. + // + // Neither EIGEN_SPARSE_QR not SUITE_SPARSE_QR are capable of + // computing the covariance if the Jacobian is rank deficient. CovarianceAlgorithmType algorithm_type; // If the Jacobian matrix is near singular, then inverting J'J @@ -294,29 +270,13 @@ class Covariance { // where min_sigma and max_sigma are the minimum and maxiumum // singular values of J respectively. // - // 2. SPARSE_CHOLESKY - // - // cholmod_rcond < min_reciprocal_conditioner_number - // - // Here cholmod_rcond is a crude estimate of the reciprocal - // condition number of J'J by using the maximum and minimum - // diagonal entries of the Cholesky factor R. There are no - // theoretical guarantees associated with this test. It can - // give false positives and negatives. Use at your own - // risk. The default value of min_reciprocal_condition_number - // has been set to a conservative value, and sometimes the - // Covariance::Compute may return false even if it is possible - // to estimate the covariance reliably. In such cases, the user - // should exercise their judgement before lowering the value of - // min_reciprocal_condition_number. - // - // 3. SPARSE_QR + // 2. SUITE_SPARSE_QR and EIGEN_SPARSE_QR // // rank(J) < num_col(J) // // Here rank(J) is the estimate of the rank of J returned by the - // SuiteSparseQR algorithm. It is a fairly reliable indication - // of rank deficiency. + // sparse QR factorization algorithm. It is a fairly reliable + // indication of rank deficiency. // double min_reciprocal_condition_number; @@ -351,8 +311,8 @@ class Covariance { // // lambda_i / lambda_max < min_reciprocal_condition_number. // - // This option has no effect on the SPARSE_CHOLESKY or SPARSE_QR - // algorithms. + // This option has no effect on the SUITE_SPARSE_QR and + // EIGEN_SPARSE_QR algorithms. int null_space_rank; int num_threads; @@ -419,4 +379,6 @@ class Covariance { } // namespace ceres +#include "ceres/internal/reenable_warnings.h" + #endif // CERES_PUBLIC_COVARIANCE_H_ |