Here are the data types with brief descriptions:

[detail level 12]

▼Nlinalg	Provides a set of common linear algebra routines
Ccholesky_factor	Computes the Cholesky factorization of a symmetric, positive definite matrix
Ccholesky_rank1_downdate	Computes the rank 1 downdate to a Cholesky factored matrix (upper triangular)
Ccholesky_rank1_update	Computes the rank 1 update to a Cholesky factored matrix (upper triangular)
Cdet	Computes the determinant of a square matrix
Cdiag_mtx_mult	Multiplies a diagonal matrix with another matrix or array
Ceigen	Computes the eigenvalues, and optionally the eigenvectors, of a matrix
Cform_lq	Forms the orthogonal matrix Q from the elementary reflectors returned by the LQ factorization algorithm
Cform_lu	Extracts the L and U matrices from the condensed [L\U] storage format used by the lu_factor
Cform_qr	Forms the full M-by-M orthogonal matrix Q from the elementary reflectors returned by the base QR factorization algorithm
Clq_factor	Computes the LQ factorization of an M-by-N matrix
Clu_factor	Computes the LU factorization of an M-by-N matrix
Cmtx_inverse	Computes the inverse of a square matrix
Cmtx_mult	Performs the matrix operation: \( C = \alpha op(A) op(B) + \beta C \)
Cmtx_pinverse	Computes the Moore-Penrose pseudo-inverse of a M-by-N matrix using the singular value decomposition of the matrix
Cmtx_rank	Computes the rank of a matrix
Cmult_lq	Multiplies a general matrix by the orthogonal matrix Q from a LQ factorization
Cmult_qr	Multiplies a general matrix by the orthogonal matrix Q from a QR factorization
Cmult_rz	Multiplies a general matrix by the orthogonal matrix Z from an RZ factorization
Cqr_factor	Computes the QR factorization of an M-by-N matrix
Cqr_rank1_update	Computes the rank 1 update to an M-by-N QR factored matrix A (M >= N) where \( A = Q R \), and \( A1 = A + U V^T \) such that \( A1 = Q1 R1 \). In the event \( V \) is complex-valued, \( V^H \) is computed instead of \( V^T \)
Crank1_update	Performs the rank-1 update to matrix A such that: \( A = \alpha X Y^T + A \), where \( A \) is an M-by-N matrix, \( \alpha \)is a scalar, \( X \) is an M-element array, and \( Y \) is an N-element array. In the event that \( Y \) is complex, \( Y^H \) is used instead of \( Y^T \)
Crecip_mult_array	Multiplies a vector by the reciprocal of a real scalar
Crz_factor	Factors an upper trapezoidal matrix by means of orthogonal transformations such that \( A = R Z = (R 0) Z \). Z is an orthogonal matrix of dimension N-by-N, and R is an M-by-M upper triangular matrix
Csolve_cholesky	Solves a system of Cholesky factored equations
Csolve_least_squares	Solves the overdetermined or underdetermined system \( A X = B \) of M equations of N unknowns. Notice, it is assumed that matrix A has full rank
Csolve_least_squares_full	Solves the overdetermined or underdetermined system \( A X = B \) of M equations of N unknowns, but uses a full orthogonal factorization of the system
Csolve_least_squares_svd	Solves the overdetermined or underdetermined system \( A X = B \) of M equations of N unknowns using a singular value decomposition of matrix A
Csolve_lq	Solves a system of M LQ-factored equations of N unknowns. N must be greater than or equal to M
Csolve_lu	Solves a system of LU-factored equations
Csolve_qr	Solves a system of M QR-factored equations of N unknowns
Csolve_triangular_system	Solves a triangular system of equations
Csort	Sorts an array
Csvd	Computes the singular value decomposition of a matrix A. The SVD is defined as: \( A = U S V^T \), where \( U \) is an M-by-M orthogonal matrix, \( S \) is an M-by-N diagonal matrix, and \( V \) is an N-by-N orthogonal matrix. In the event that \( V \) is complex valued, \( V^H \) is computed instead of \( V^T \)
Cswap	Swaps the contents of two arrays
Ctrace	Computes the trace of a matrix (the sum of the main diagonal elements)
Ctri_mtx_mult	Computes the triangular matrix operation: \( B = \alpha A^T A + \beta B \), or \( B = \alpha A A^T + \beta B \), where A is a triangular matrix