# matrix¶

The matrix module defines an abstract Matrix object and several implementations. Matrix objects support basic addition and subtraction operations and provide a consistent insterface for solving linear systems. Matrices can be converted into other forms suitable for external processing via the export method.

exception nutils.matrix.BackendNotAvailable

Error message reporting that the selected matrix backend is not available on the system.

class nutils.matrix.Matrix(shape, dtype)

Bases: object

matrix base class

T

transpose matrix

__add__(self, other)

__matmul__(self, other)

multiply matrix with a dense tensor

__mul__(self, other)

multiply matrix with a scalar

__neg__(self)

negate matrix

__weakref__

list of weak references to the object (if defined)

export(self, form)

Export matrix data to any of supported forms.

Parameters: form (str) – “dense” : return matrix as a single dense array ”csr” : return matrix as 3-tuple of (data, indices, indptr) ”coo” : return matrix as 2-tuple of (data, (row, col))
rowsupp(self, tol=0)

return row indices with nonzero/non-small entries

solve(self, rhs=None, *, lhs0=None, constrain=None, rconstrain=None, solver='arnoldi', atol=0.0, rtol=0.0, **solverargs)

Solve system given right hand side vector and/or constraints.

Parameters: rhs (float vector or None) – Right hand side vector. A None value implies the zero vector. lhs0 (class:float vector or None) – Initial values: compute the solution by solving A dx = b - A lhs0. A None value implies the zero vector, i.e. solving A x = b directly. constrain (float or bool array, or None Column) – constraints. For float values, a number signifies a constraint, NaN signifies a free dof. For boolean, a True value signifies a constraint to the value in lhs0, a False value signifies a free dof. A None value implies no constraints. rconstrain (bool array or None) – Row constrains. A True value signifies a constrains, a False value a free dof. A None value implies that the constraints follow those defined in constrain (by implication the matrix must be square). solver (str) – Name of the solver algorithm. The set of available solvers depends on the type of the matrix (i.e. the active backend), although the ‘direct’ and ‘arnoldi’ solvers are always available. rtol (float) – Relative tolerance: see atol. atol (float) – Absolute tolerance: require that |A x - b| <= max(atol, rtol |b|) after applying constraints and the initial value. In case atol and rtol are both zero (the defaults) solve to machine precision. Otherwise fail with nutils.matrix.ToleranceNotReached if the requirement is not reached. **kwargs – All remaining arguments are passed on to the selected solver method. Left hand side vector. numpy.ndarray
solve_leniently(self, *args, **kwargs)

Identical to nutils.matrix.Matrix.solve(), but emit a warning in case tolerances are not met rather than an exception, while returning the obtained solution vector.

submatrix(self, rows, cols)

Create submatrix from selected rows, columns.

Parameters: rows (bool/int array selecting rows for keeping) – cols (bool/int array selecting columns for keeping) – Matrix instance of reduced dimensions Matrix
exception nutils.matrix.MatrixError

Bases: Exception

General error message for matrix-related failure.

__weakref__

list of weak references to the object (if defined)

exception nutils.matrix.ToleranceNotReached(best)

Error message reporting that the configured linear solver tolerance was not reached. The .best attribute carries the non-conforming solution.