adelie.adelie_core.matrix.MatrixConstraintSparse64C#

class adelie.adelie_core.matrix.MatrixConstraintSparse64C#

Core matrix class for sparse constraint matrix.

Methods

`__init__`(self, rows, cols, nnz, outer, ...)
`cols`(self)	Returns the number of columns.
`cov`(self, arg0, arg1)	Computes the covariance matrix.
`mul`(self, arg0, arg1)	Computes a matrix-vector multiplication.
`rmmul`(self, arg0, arg1, arg2)	Computes a row vector-matrix multiplication.
`rows`(self)	Returns the number of rows.
`rvmul`(self, arg0, arg1)	Computes a row vector-vector multiplication.
`rvtmul`(self, arg0, arg1, arg2)	Computes a row vector-scalar multiplication increment.
`sp_mul`(self, arg0, arg1, arg2)	Computes a matrix-sparse vector multiplication.
`tmul`(self, arg0, arg1)	Computes a matrix transpose-vector multiplication.

Attributes

`ndim`	Number of dimensions.
`shape`	Shape of the matrix.

__init__(self: adelie.adelie_core.matrix.MatrixConstraintSparse64C, rows: int, cols: int, nnz: int, outer: numpy.ndarray[numpy.int32[1, n]], inner: numpy.ndarray[numpy.int32[1, n]], value: numpy.ndarray[numpy.float64[1, n]], n_threads: int) → None#

cols(self: adelie.adelie_core.matrix.MatrixConstraintBase64) → int#

Returns the number of columns.

Returns:

colsint: Number of columns.

cov(self: adelie.adelie_core.matrix.MatrixConstraintBase64, arg0: numpy.ndarray[numpy.float64[m, n], flags.f_contiguous], arg1: numpy.ndarray[numpy.float64[m, n], flags.writeable, flags.f_contiguous]) → None#

Computes the covariance matrix.

Computes the covariance matrix A @ Q @ A.T.

Parameters:

Q(d, d) ndarray: Matrix of weights.
out(m, m) ndarray: Matrix to store in-place the result.

mul(self: adelie.adelie_core.matrix.MatrixConstraintBase64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a matrix-vector multiplication.

Computes the matrix-vector multiplication v.T @ A.

Parameters:

v(m,) ndarray: Vector to multiply with the matrix.
out(d,) ndarray: Vector to store in-place the result.

rmmul(self: adelie.adelie_core.matrix.MatrixConstraintBase64, arg0: int, arg1: numpy.ndarray[numpy.float64[m, n], flags.f_contiguous], arg2: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a row vector-matrix multiplication.

Computes the matrix-vector multiplication A[j].T @ Q.

Parameters:

jint: Row index.
Q(d, d) ndarray: Matrix to dot product with the j th row.
out(d,) ndarray: Vector to store in-place the result.

rows(self: adelie.adelie_core.matrix.MatrixConstraintBase64) → int#

Returns the number of rows.

Returns:

rowsint: Number of rows.

rvmul(self: adelie.adelie_core.matrix.MatrixConstraintBase64, arg0: int, arg1: numpy.ndarray[numpy.float64[1, n]]) → float#

Computes a row vector-vector multiplication.

Computes the dot-product A[j].T @ v.

Parameters:

jint: Row index.
v(d,) ndarray: Vector to dot product with the j th row.

Returns:

dotfloat: Row vector-vector multiplication.

rvtmul(self: adelie.adelie_core.matrix.MatrixConstraintBase64, arg0: int, arg1: float, arg2: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a row vector-scalar multiplication increment.

Computes the vector-scalar multiplication A[j] * v. The result is incremented into the output vector.

Parameters:

jint: Row index.
vfloat: Scalar to multiply with the j th row.
out(d,) ndarray: Vector to increment in-place the result.

sp_mul(self: adelie.adelie_core.matrix.MatrixConstraintBase64, arg0: numpy.ndarray[numpy.int64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n]], arg2: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a matrix-sparse vector multiplication.

Computes the matrix-sparse vector multiplication v.T @ A where v is represented by the sparse-format indices and values.

Parameters:

indices(nnz,) ndarray: Vector of indices with non-zero values of v. It does not have to be sorted in increasing order.
values(nnz,) ndarray: Vector of values corresponding to indices.
out(d,) ndarray: Vector to store in-place the result.

tmul(self: adelie.adelie_core.matrix.MatrixConstraintBase64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a matrix transpose-vector multiplication.

Computes the matrix transpose-vector multiplication v.T @ A.T.

Parameters:

v(d,) ndarray: Vector to multiply with the matrix.
out(m,) ndarray: Vector to store in-place the result.

ndim#: Number of dimensions. It is always 2.

shape#: Shape of the matrix.