d_mat.h – double precision matrices¶

Memory management¶

void d_mat_init(d_mat_t mat, slong rows, slong cols)¶: Initialises a matrix with the given number of rows and columns for use.

void d_mat_clear(d_mat_t mat)¶: Clears the given matrix.

Basic assignment and manipulation¶

void d_mat_set(d_mat_t mat1, const d_mat_t mat2)¶: Sets mat1 to a copy of mat2. The dimensions of mat1 and mat2 must be the same.

void d_mat_swap(d_mat_t mat1, d_mat_t mat2)¶: Swaps two matrices. The dimensions of mat1 and mat2 are allowed to be different.

double d_mat_entry(d_mat_t mat, slong i, slong j)¶: Returns the entry of mat at row \(i\) and column \(j\). Both \(i\) and \(j\) must not exceed the dimensions of the matrix. This function is implemented as a macro.

double d_mat_get_entry(const d_mat_t mat, slong i, slong j)¶: Returns the entry of mat at row \(i\) and column \(j\). Both \(i\) and \(j\) must not exceed the dimensions of the matrix.

double * d_mat_entry_ptr(const d_mat_t mat, slong i, slong j)¶: Returns a pointer to the entry of mat at row \(i\) and column \(j\). Both \(i\) and \(j\) must not exceed the dimensions of the matrix.

void d_mat_zero(d_mat_t mat)¶: Sets all entries of mat to 0.

void d_mat_one(d_mat_t mat)¶: Sets mat to the unit matrix, having ones on the main diagonal and zeroes elsewhere. If mat is nonsquare, it is set to the truncation of a unit matrix.

Random matrix generation¶

void d_mat_randtest(d_mat_t mat, flint_rand_t state, slong minexp, slong maxexp)¶: Sets the entries of mat to random signed numbers with exponents between minexp and maxexp or zero.

Input and output¶

void d_mat_print(const d_mat_t mat)¶: Prints the given matrix to the stream stdout.

Comparison¶

int d_mat_equal(const d_mat_t mat1, const d_mat_t mat2)¶: Returns a non-zero value if mat1 and mat2 have the same dimensions and entries, and zero otherwise.

int d_mat_approx_equal(const d_mat_t mat1, const d_mat_t mat2, double eps)¶: Returns a non-zero value if mat1 and mat2 have the same dimensions and entries within eps of each other, and zero otherwise.

int d_mat_is_zero(const d_mat_t mat)¶: Returns a non-zero value if all entries mat are zero, and otherwise returns zero.

int d_mat_is_approx_zero(const d_mat_t mat, double eps)¶: Returns a non-zero value if all entries mat are zero to within eps and otherwise returns zero.

int d_mat_is_empty(const d_mat_t mat)¶: Returns a non-zero value if the number of rows or the number of columns in mat is zero, and otherwise returns zero.

int d_mat_is_square(const d_mat_t mat)¶: Returns a non-zero value if the number of rows is equal to the number of columns in mat, and otherwise returns zero.

Transpose¶

void d_mat_transpose(d_mat_t B, const d_mat_t A)¶: Sets \(B\) to \(A^T\), the transpose of \(A\). Dimensions must be compatible. \(A\) and \(B\) are allowed to be the same object if \(A\) is a square matrix.

Matrix multiplication¶

void d_mat_mul_classical(d_mat_t C, const d_mat_t A, const d_mat_t B)¶: Sets C to the matrix product \(C = A B\). The matrices must have compatible dimensions for matrix multiplication (an exception is raised otherwise). Aliasing is allowed.

Gram-Schmidt Orthogonalisation and QR Decomposition¶

void d_mat_gso(d_mat_t B, const d_mat_t A)¶

Takes a subset of \(R^m\) \(S = {a_1, a_2, \ldots, a_n}\) (as the columns of a \(m x n\) matrix A) and generates an orthonormal set \(S^' = {b_1, b_2, \ldots, b_n}\) (as the columns of the \(m x n\) matrix B) that spans the same subspace of \(R^m\) as \(S\).

This uses an algorithm of Schwarz-Rutishauser. See pp. 9 of url{http://www.inf.ethz.ch/personal/gander/papers/qrneu.pdf}

void d_mat_qr(d_mat_t Q, d_mat_t R, const d_mat_t A)¶

Computes the \(QR\) decomposition of a matrix A using the Gram-Schmidt process. (Sets Q and R such that \(A = QR\) where R is an upper triangular matrix and Q is an orthogonal matrix.)