# ca_mat.h – matrices over the real and complex numbers¶

A ca_mat_t represents a dense matrix over the real or complex numbers, implemented as an array of entries of type ca_struct. The dimension (number of rows and columns) of a matrix is fixed at initialization, and the user must ensure that inputs and outputs to an operation have compatible dimensions. The number of rows or columns in a matrix can be zero.

## Types, macros and constants¶

type ca_mat_struct
type ca_mat_t

Contains a pointer to a flat array of the entries (entries), an array of pointers to the start of each row (rows), and the number of rows (r) and columns (c).

A ca_mat_t is defined as an array of length one of type ca_mat_struct, permitting a ca_mat_t to be passed by reference.

ca_mat_entry(mat, i, j)

Macro giving a pointer to the entry at row i and column j.

ca_mat_nrows(mat)

Returns the number of rows of the matrix.

ca_mat_ncols(mat)

Returns the number of columns of the matrix.

ca_ptr ca_mat_entry_ptr(ca_mat_t mat, slong i, slong j)

Returns a pointer to the entry at row i and column j. Equivalent to ca_mat_entry but implemented as a function.

## Memory management¶

void ca_mat_init(ca_mat_t mat, slong r, slong c, ca_ctx_t ctx)

Initializes the matrix, setting it to the zero matrix with r rows and c columns.

void ca_mat_clear(ca_mat_t mat, ca_ctx_t ctx)

Clears the matrix, deallocating all entries.

void ca_mat_swap(ca_mat_t mat1, ca_mat_t mat2, ca_ctx_t ctx)

Efficiently swaps mat1 and mat2.

void ca_mat_window_init(ca_mat_t window, const ca_mat_t mat, slong r1, slong c1, slong r2, slong c2, ca_ctx_t ctx)

Initializes window to a window matrix into the submatrix of mat starting at the corner at row r1 and column c1 (inclusive) and ending at row r2 and column c2 (exclusive).

void ca_mat_window_clear(ca_mat_t window, ca_ctx_t ctx)

Frees the window matrix.

## Assignment and conversions¶

void ca_mat_set(ca_mat_t dest, const ca_mat_t src, ca_ctx_t ctx)
void ca_mat_set_fmpz_mat(ca_mat_t dest, const fmpz_mat_t src, ca_ctx_t ctx)
void ca_mat_set_fmpq_mat(ca_mat_t dest, const fmpq_mat_t src, ca_ctx_t ctx)

Sets dest to src. The operands must have identical dimensions.

void ca_mat_set_ca(ca_mat_t mat, const ca_t c, ca_ctx_t ctx)

Sets mat to the matrix with the scalar c on the main diagonal and zeros elsewhere.

void ca_mat_transfer(ca_mat_t res, ca_ctx_t res_ctx, const ca_mat_t src, ca_ctx_t src_ctx)

Sets res to src where the corresponding context objects res_ctx and src_ctx may be different.

This operation preserves the mathematical value represented by src, but may result in a different internal representation depending on the settings of the context objects.

## Random generation¶

void ca_mat_randtest(ca_mat_t mat, flint_rand_t state, slong depth, slong bits, ca_ctx_t ctx)

Sets mat to a random matrix with entries having complexity up to depth and bits (see ca_randtest()).

void ca_mat_randtest_rational(ca_mat_t mat, flint_rand_t state, slong bits, ca_ctx_t ctx)

Sets mat to a random rational matrix with entries up to bits bits in size.

void ca_mat_randops(ca_mat_t mat, flint_rand_t state, slong count, ca_ctx_t ctx)

Randomizes mat in-place by performing elementary row or column operations. More precisely, at most count random additions or subtractions of distinct rows and columns will be performed. This leaves the rank (and for square matrices, the determinant) unchanged.

## Input and output¶

void ca_mat_print(const ca_mat_t mat, ca_ctx_t ctx)

Prints mat to standard output. The entries are printed on separate lines.

void ca_mat_printn(const ca_mat_t mat, slong digits, ca_ctx_t ctx)

Prints a decimal representation of mat with precision specified by digits. The entries are comma-separated with square brackets and comma separation for the rows.

## Special matrices¶

void ca_mat_zero(ca_mat_t mat, ca_ctx_t ctx)

Sets all entries in mat to zero.

void ca_mat_one(ca_mat_t mat, ca_ctx_t ctx)

Sets the entries on the main diagonal of mat to one, and all other entries to zero.

void ca_mat_ones(ca_mat_t mat, ca_ctx_t ctx)

Sets all entries in mat to one.

void ca_mat_pascal(ca_mat_t mat, int triangular, ca_ctx_t ctx)

Sets mat to a Pascal matrix, whose entries are binomial coefficients. If triangular is 0, constructs a full symmetric matrix with the rows of Pascal’s triangle as successive antidiagonals. If triangular is 1, constructs the upper triangular matrix with the rows of Pascal’s triangle as columns, and if triangular is -1, constructs the lower triangular matrix with the rows of Pascal’s triangle as rows.

void ca_mat_stirling(ca_mat_t mat, int kind, ca_ctx_t ctx)

Sets mat to a Stirling matrix, whose entries are Stirling numbers. If kind is 0, the entries are set to the unsigned Stirling numbers of the first kind. If kind is 1, the entries are set to the signed Stirling numbers of the first kind. If kind is 2, the entries are set to the Stirling numbers of the second kind.

void ca_mat_hilbert(ca_mat_t mat, ca_ctx_t ctx)

Sets mat to the Hilbert matrix, which has entries $$A_{i,j} = 1/(i+j+1)$$.

void ca_mat_dft(ca_mat_t mat, int type, ca_ctx_t ctx)

Sets mat to the DFT (discrete Fourier transform) matrix of order n where n is the smallest dimension of mat (if mat is not square, the matrix is extended periodically along the larger dimension). The type parameter selects between four different versions of the DFT matrix (in which $$\omega = e^{2\pi i/n}$$):

• Type 0 – entries $$A_{j,k} = \omega^{-jk}$$

• Type 1 – entries $$A_{j,k} = \omega^{jk} / n$$

• Type 2 – entries $$A_{j,k} = \omega^{-jk} / \sqrt{n}$$

• Type 3 – entries $$A_{j,k} = \omega^{jk} / \sqrt{n}$$

The type 0 and 1 matrices are inverse pairs, and similarly for the type 2 and 3 matrices.

## Comparisons and properties¶

truth_t ca_mat_check_equal(const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)

Compares A and B for equality.

truth_t ca_mat_check_is_zero(const ca_mat_t A, ca_ctx_t ctx)

Tests if A is the zero matrix.

truth_t ca_mat_check_is_one(const ca_mat_t A, ca_ctx_t ctx)

Tests if A has ones on the main diagonal and zeros elsewhere.

## Conjugate and transpose¶

void ca_mat_transpose(ca_mat_t res, const ca_mat_t A, ca_ctx_t ctx)

Sets res to the transpose of A.

void ca_mat_conj(ca_mat_t res, const ca_mat_t A, ca_ctx_t ctx)

Sets res to the entrywise complex conjugate of A.

void ca_mat_conj_transpose(ca_mat_t res, const ca_mat_t A, ca_ctx_t ctx)

Sets res to the conjugate transpose (Hermitian transpose) of A.

## Arithmetic¶

void ca_mat_neg(ca_mat_t res, const ca_mat_t A, ca_ctx_t ctx)

Sets res to the negation of A.

void ca_mat_add(ca_mat_t res, const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)

Sets res to the sum of A and B.

void ca_mat_sub(ca_mat_t res, const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)

Sets res to the difference of A and B.

void ca_mat_mul_classical(ca_mat_t res, const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)
void ca_mat_mul_same_nf(ca_mat_t res, const ca_mat_t A, const ca_mat_t B, ca_field_t K, ca_ctx_t ctx)
void ca_mat_mul(ca_mat_t res, const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)

Sets res to the matrix product of A and B. The classical version uses classical multiplication. The same_nf version assumes (not checked) that both A and B have coefficients in the same simple algebraic number field K or in $$\mathbb{Q}$$. The default version chooses an algorithm automatically.

void ca_mat_mul_si(ca_mat_t B, const ca_mat_t A, slong c, ca_ctx_t ctx)
void ca_mat_mul_fmpz(ca_mat_t B, const ca_mat_t A, const fmpz_t c, ca_ctx_t ctx)
void ca_mat_mul_fmpq(ca_mat_t B, const ca_mat_t A, const fmpq_t c, ca_ctx_t ctx)
void ca_mat_mul_ca(ca_mat_t B, const ca_mat_t A, const ca_t c, ca_ctx_t ctx)

Sets B to A multiplied by the scalar c.

void ca_mat_div_si(ca_mat_t B, const ca_mat_t A, slong c, ca_ctx_t ctx)
void ca_mat_div_fmpz(ca_mat_t B, const ca_mat_t A, const fmpz_t c, ca_ctx_t ctx)
void ca_mat_div_fmpq(ca_mat_t B, const ca_mat_t A, const fmpq_t c, ca_ctx_t ctx)
void ca_mat_div_ca(ca_mat_t B, const ca_mat_t A, const ca_t c, ca_ctx_t ctx)

Sets B to A divided by the scalar c.

void ca_mat_add_ca(ca_mat_t B, const ca_mat_t A, const ca_t c, ca_ctx_t ctx)
void ca_mat_sub_ca(ca_mat_t B, const ca_mat_t A, const ca_t c, ca_ctx_t ctx)

Sets B to A plus or minus the scalar c (interpreted as a diagonal matrix).

void ca_mat_addmul_ca(ca_mat_t B, const ca_mat_t A, const ca_t c, ca_ctx_t ctx)
void ca_mat_submul_ca(ca_mat_t B, const ca_mat_t A, const ca_t c, ca_ctx_t ctx)

Sets the matrix B to B plus (or minus) the matrix A multiplied by the scalar c.

## Powers¶

void ca_mat_sqr(ca_mat_t B, const ca_mat_t A, ca_ctx_t ctx)

Sets B to the square of A.

void ca_mat_pow_ui_binexp(ca_mat_t B, const ca_mat_t A, ulong exp, ca_ctx_t ctx)

Sets B to A raised to the power exp, evaluated using binary exponentiation.

## Polynomial evaluation¶

void _ca_mat_ca_poly_evaluate(ca_mat_t res, ca_srcptr poly, slong len, const ca_mat_t A, ca_ctx_t ctx)
void ca_mat_ca_poly_evaluate(ca_mat_t res, const ca_poly_t poly, const ca_mat_t A, ca_ctx_t ctx)

Sets res to $$f(A)$$ where f is the polynomial given by poly and A is a square matrix. Uses the Paterson-Stockmeyer algorithm.

## Gaussian elimination and LU decomposition¶

truth_t ca_mat_find_pivot(slong *pivot_row, ca_mat_t mat, slong start_row, slong end_row, slong column, ca_ctx_t ctx)

Attempts to find a nonzero entry in mat with column index column and row index between start_row (inclusive) and end_row (exclusive).

If the return value is T_TRUE, such an element exists, and pivot_row is set to the row index. If the return value is T_FALSE, no such element exists (all entries in this part of the column are zero). If the return value is T_UNKNOWN, it is unknown whether such an element exists (zero certification failed).

This function is destructive: any elements that are nontrivially zero but can be certified zero will be overwritten by exact zeros.

int ca_mat_lu_classical(slong *rank, slong *P, ca_mat_t LU, const ca_mat_t A, int rank_check, ca_ctx_t ctx)
int ca_mat_lu_recursive(slong *rank, slong *P, ca_mat_t LU, const ca_mat_t A, int rank_check, ca_ctx_t ctx)
int ca_mat_lu(slong *rank, slong *P, ca_mat_t LU, const ca_mat_t A, int rank_check, ca_ctx_t ctx)

Computes a generalized LU decomposition $$A = PLU$$ of a given matrix A, writing the rank of A to rank.

If A is a nonsingular square matrix, LU will be set to a unit diagonal lower triangular matrix L and an upper triangular matrix U (the diagonal of L will not be stored explicitly).

If A is an arbitrary matrix of rank r, U will be in row echelon form having r nonzero rows, and L will be lower triangular but truncated to r columns, having implicit ones on the r first entries of the main diagonal. All other entries will be zero.

If a nonzero value for rank_check is passed, the function will abandon the output matrix in an undefined state and set the rank to 0 if A is detected to be rank-deficient.

The algorithm can fail if it fails to certify that a pivot element is zero or nonzero, in which case the correct rank cannot be determined. The return value is 1 on success and 0 on failure. On failure, the data in the output variables rank, P and LU will be meaningless.

The classical version uses iterative Gaussian elimination. The recursive version uses a block recursive algorithm to take advantage of fast matrix multiplication.

int ca_mat_fflu(slong *rank, slong *P, ca_mat_t LU, ca_t den, const ca_mat_t A, int rank_check, ca_ctx_t ctx)

Similar to ca_mat_lu(), but computes a fraction-free LU decomposition using the Bareiss algorithm. The denominator is written to den. Note that despite being “fraction-free”, this algorithm may introduce fractions due to incomplete symbolic simplifications.

truth_t ca_mat_nonsingular_lu(slong *P, ca_mat_t LU, const ca_mat_t A, ca_ctx_t ctx)

Wrapper for ca_mat_lu(). If A can be proved to be invertible/nonsingular, returns T_TRUE and sets P and LU to a LU decomposition $$A = PLU$$. If A can be proved to be singular, returns T_FALSE. If A cannot be proved to be either singular or nonsingular, returns T_UNKNOWN. When the return value is T_FALSE or T_UNKNOWN, the LU factorization is not completed and the values of P and LU are arbitrary.

truth_t ca_mat_nonsingular_fflu(slong *P, ca_mat_t LU, ca_t den, const ca_mat_t A, ca_ctx_t ctx)

Wrapper for ca_mat_fflu(). Similar to ca_mat_nonsingular_lu(), but computes a fraction-free LU decomposition using the Bareiss algorithm. The denominator is written to den. Note that despite being “fraction-free”, this algorithm may introduce fractions due to incomplete symbolic simplifications.

## Solving and inverse¶

truth_t ca_mat_inv(ca_mat_t X, const ca_mat_t A, ca_ctx_t ctx)

Determines if the square matrix A is nonsingular, and if successful, sets $$X = A^{-1}$$ and returns T_TRUE. Returns T_FALSE if A is singular, and T_UNKNOWN if the rank of A cannot be determined.

truth_t ca_mat_nonsingular_solve_adjugate(ca_mat_t X, const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)
truth_t ca_mat_nonsingular_solve_fflu(ca_mat_t X, const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)
truth_t ca_mat_nonsingular_solve_lu(ca_mat_t X, const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)
truth_t ca_mat_nonsingular_solve(ca_mat_t X, const ca_mat_t A, const ca_mat_t B, ca_ctx_t ctx)

Determines if the square matrix A is nonsingular, and if successful, solves $$AX = B$$ and returns T_TRUE. Returns T_FALSE if A is singular, and T_UNKNOWN if the rank of A cannot be determined.

void ca_mat_solve_tril_classical(ca_mat_t X, const ca_mat_t L, const ca_mat_t B, int unit, ca_ctx_t ctx)
void ca_mat_solve_tril_recursive(ca_mat_t X, const ca_mat_t L, const ca_mat_t B, int unit, ca_ctx_t ctx)
void ca_mat_solve_tril(ca_mat_t X, const ca_mat_t L, const ca_mat_t B, int unit, ca_ctx_t ctx)
void ca_mat_solve_triu_classical(ca_mat_t X, const ca_mat_t U, const ca_mat_t B, int unit, ca_ctx_t ctx)
void ca_mat_solve_triu_recursive(ca_mat_t X, const ca_mat_t U, const ca_mat_t B, int unit, ca_ctx_t ctx)
void ca_mat_solve_triu(ca_mat_t X, const ca_mat_t U, const ca_mat_t B, int unit, ca_ctx_t ctx)

Solves the lower triangular system $$LX = B$$ or the upper triangular system $$UX = B$$, respectively. It is assumed (not checked) that the diagonal entries are nonzero. If unit is set, the main diagonal of L or U is taken to consist of all ones, and in that case the actual entries on the diagonal are not read at all and can contain other data.

The classical versions perform the computations iteratively while the recursive versions perform the computations in a block recursive way to benefit from fast matrix multiplication. The default versions choose an algorithm automatically.

void ca_mat_solve_fflu_precomp(ca_mat_t X, const slong *perm, const ca_mat_t A, const ca_t den, const ca_mat_t B, ca_ctx_t ctx)
void ca_mat_solve_lu_precomp(ca_mat_t X, const slong *P, const ca_mat_t LU, const ca_mat_t B, ca_ctx_t ctx)

Solves $$AX = B$$ given the precomputed nonsingular LU decomposition $$A = PLU$$ or fraction-free LU decomposition with denominator den. The matrices $$X$$ and $$B$$ are allowed to be aliased with each other, but $$X$$ is not allowed to be aliased with $$LU$$.

## Rank and echelon form¶

int ca_mat_rank(slong *rank, const ca_mat_t A, ca_ctx_t ctx)

Computes the rank of the matrix A. If successful, returns 1 and writes the rank to rank. If unsuccessful, returns 0.

int ca_mat_rref_fflu(slong *rank, ca_mat_t R, const ca_mat_t A, ca_ctx_t ctx)
int ca_mat_rref_lu(slong *rank, ca_mat_t R, const ca_mat_t A, ca_ctx_t ctx)
int ca_mat_rref(slong *rank, ca_mat_t R, const ca_mat_t A, ca_ctx_t ctx)

Computes the reduced row echelon form (rref) of a given matrix. On success, sets R to the rref of A, writes the rank to rank, and returns 1. On failure to certify the correct rank, returns 0, leaving the data in rank and R meaningless.

The fflu version computes a fraction-free LU decomposition and then converts the output ro rref form. The lu version computes a regular LU decomposition and then converts the output to rref form. The default version uses an automatic algorithm choice and may implement additional methods for special cases.

int ca_mat_right_kernel(ca_mat_t X, const ca_mat_t A, ca_ctx_t ctx)

Sets X to a basis of the right kernel (nullspace) of A. The output matrix X will be resized in-place to have a number of columns equal to the nullity of A. Returns 1 on success. On failure, returns 0 and leaves the data in X meaningless.

## Determinant and trace¶

void ca_mat_trace(ca_t trace, const ca_mat_t mat, ca_ctx_t ctx)

Sets trace to the sum of the entries on the main diagonal of mat.

void ca_mat_det_berkowitz(ca_t det, const ca_mat_t A, ca_ctx_t ctx)
int ca_mat_det_lu(ca_t det, const ca_mat_t A, ca_ctx_t ctx)
int ca_mat_det_bareiss(ca_t det, const ca_mat_t A, ca_ctx_t ctx)
void ca_mat_det_cofactor(ca_t det, const ca_mat_t A, ca_ctx_t ctx)
void ca_mat_det(ca_t det, const ca_mat_t A, ca_ctx_t ctx)

Sets det to the determinant of the square matrix A. Various algorithms are available:

• The berkowitz version uses the division-free Berkowitz algorithm performing $$O(n^4)$$ operations. Since no zero tests are required, it is guaranteed to succeed.

• The cofactor version performs cofactor expansion. This is currently only supported for matrices up to size 4.

• The lu and bareiss versions use rational LU decomposition and fraction-free LU decomposition (Bareiss algorithm) respectively, requiring $$O(n^3)$$ operations. These algorithms can fail if zero certification fails (see ca_mat_nonsingular_lu()); they return 1 for success and 0 for failure. Note that the Bareiss algorithm, despite being “fraction-free”, may introduce fractions due to incomplete symbolic simplifications.

The default function chooses an algorithm automatically. It will, in addition, recognize trivially rational and integer matrices and evaluate those determinants using fmpq_mat_t or fmpz_mat_t.

The various algorithms can produce different symbolic forms of the same determinant. Which algorithm performs better depends strongly and sometimes unpredictably on the structure of the matrix.

void ca_mat_adjugate_cofactor(ca_mat_t adj, ca_t det, const ca_mat_t A, ca_ctx_t ctx)
void ca_mat_adjugate_charpoly(ca_mat_t adj, ca_t det, const ca_mat_t A, ca_ctx_t ctx)
void ca_mat_adjugate(ca_mat_t adj, ca_t det, const ca_mat_t A, ca_ctx_t ctx)

Sets adj to the adjuate matrix of A and det to the determinant of A, both computed simultaneously. The cofactor version uses cofactor expansion. The charpoly version computes and evaluates the characteristic polynomial. The default version uses an automatic algorithm choice.

## Characteristic polynomial¶

void _ca_mat_charpoly_berkowitz(ca_ptr cp, const ca_mat_t mat, ca_ctx_t ctx)
void ca_mat_charpoly_berkowitz(ca_poly_t cp, const ca_mat_t mat, ca_ctx_t ctx)
int _ca_mat_charpoly_danilevsky(ca_ptr cp, const ca_mat_t mat, ca_ctx_t ctx)
int ca_mat_charpoly_danilevsky(ca_poly_t cp, const ca_mat_t mat, ca_ctx_t ctx)
void _ca_mat_charpoly(ca_ptr cp, const ca_mat_t mat, ca_ctx_t ctx)
void ca_mat_charpoly(ca_poly_t cp, const ca_mat_t mat, ca_ctx_t ctx)

Sets poly to the characteristic polynomial of mat which must be a square matrix. If the matrix has n rows, the underscore method requires space for $$n + 1$$ output coefficients.

The berkowitz version uses a division-free algorithm requiring $$O(n^4)$$ operations. The danilevsky version only performs $$O(n^3)$$ operations, but performs divisions and needs to check for zero which can fail. This version returns 1 on success and 0 on failure. The default version chooses an algorithm automatically.

int ca_mat_companion(ca_mat_t mat, const ca_poly_t poly, ca_ctx_t ctx)

Sets mat to the companion matrix of poly. This function verifies that the leading coefficient of poly is provably nonzero and that the output matrix has the right size, returning 1 on success. It returns 0 if the leading coefficient of poly cannot be proved nonzero or if the size of the output matrix does not match.

## Eigenvalues and eigenvectors¶

int ca_mat_eigenvalues(ca_vec_t lambda, ulong *exp, const ca_mat_t mat, ca_ctx_t ctx)

Attempts to compute all complex eigenvalues of the given matrix mat. On success, returns 1 and sets lambda to the distinct eigenvalues with corresponding multiplicities in exp. The eigenvalues are returned in arbitrary order. On failure, returns 0 and leaves the values in lambda and exp arbitrary.

This function effectively computes the characteristic polynomial and then calls ca_poly_roots.

truth_t ca_mat_diagonalization(ca_mat_t D, ca_mat_t P, const ca_mat_t A, ca_ctx_t ctx)

Matrix diagonalization: attempts to compute a diagonal matrix D and an invertible matrix P such that $$A = PDP^{-1}$$. Returns T_TRUE if A is diagonalizable and the computation succeeds, T_FALSE if A is provably not diagonalizable, and T_UNKNOWN if it is unknown whether A is diagonalizable. If the return value is not T_TRUE, the values in D and P are arbitrary.

## Jordan canonical form¶

int ca_mat_jordan_blocks(ca_vec_t lambda, slong *num_blocks, slong *block_lambda, slong *block_size, const ca_mat_t A, ca_ctx_t ctx)

Computes the blocks of the Jordan canonical form of A. On success, returns 1 and sets lambda to the unique eigenvalues of A, sets num_blocks to the number of Jordan blocks, entry i of block_lambda to the index of the eigenvalue in Jordan block i, and entry i of block_size to the size of Jordan block i. On failure, returns 0, leaving arbitrary values in the output variables. The user should allocate space in block_lambda and block_size for up to n entries where n is the size of the matrix.

The Jordan form is unique up to the ordering of blocks, which is arbitrary.

void ca_mat_set_jordan_blocks(ca_mat_t mat, const ca_vec_t lambda, slong num_blocks, slong *block_lambda, slong *block_size, ca_ctx_t ctx)

Sets mat to the concatenation of the Jordan blocks given in lambda, num_blocks, block_lambda and block_size. See ca_mat_jordan_blocks() for an explanation of these variables.

int ca_mat_jordan_transformation(ca_mat_t mat, const ca_vec_t lambda, slong num_blocks, slong *block_lambda, slong *block_size, const ca_mat_t A, ca_ctx_t ctx)

Given the precomputed Jordan block decomposition (lambda, num_blocks, block_lambda, block_size) of the square matrix A, computes the corresponding transformation matrix P such that $$A = P J P^{-1}$$. On success, writes P to mat and returns 1. On failure, returns 0, leaving the value of mat arbitrary.

int ca_mat_jordan_form(ca_mat_t J, ca_mat_t P, const ca_mat_t A, ca_ctx_t ctx)

Computes the Jordan decomposition $$A = P J P^{-1}$$ of the given square matrix A. The user can pass NULL for the output variable P, in which case only J is computed. On success, returns 1. On failure, returns 0, leaving the values of J and P arbitrary.

This function is a convenience wrapper around ca_mat_jordan_blocks(), ca_mat_set_jordan_blocks() and ca_mat_jordan_transformation(). For computations with the Jordan decomposition, it is often better to use those methods directly since they give direct access to the spectrum and block structure.

## Matrix functions¶

int ca_mat_exp(ca_mat_t res, const ca_mat_t A, ca_ctx_t ctx)

Matrix exponential: given a square matrix A, sets res to $$e^A$$ and returns 1 on success. If unsuccessful, returns 0, leaving the values in res arbitrary.

This function uses Jordan decomposition. The matrix exponential always exists, but computation can fail if computing the Jordan decomposition fails.

truth_t ca_mat_log(ca_mat_t res, const ca_mat_t A, ca_ctx_t ctx)

Matrix logarithm: given a square matrix A, sets res to a logarithm $$\log(A)$$ and returns T_TRUE on success. If A can be proved to have no logarithm, returns T_FALSE. If the existence of a logarithm cannot be proved, returns T_UNKNOWN.

This function uses the Jordan decomposition, and the branch of the matrix logarithm is defined by taking the principal values of the logarithms of all eigenvalues.