def __init__(self, tolerance=1e-10, iterations=1000, steps=None, relaxation = 1.0, precon=None):
"""
The `Solver` class should not be invoked directly.
:Parameters:
- `tolerance`: The required error tolerance.
- `iterations`: The maximum number of iterative steps to perform.
- `steps`: A deprecated name for `iterations`.
- `relaxation`: The relaxation.
"""
PysparseSolver.__init__(self, tolerance=tolerance, iterations=iterations, steps=steps, precon=precon)
self.relaxation = relaxation
def __init__(self, tolerance=1e-10, iterations=10, steps=None, precon=None, maxIterations=10):
"""
Creates a `LinearLUSolver`.
:Parameters:
- `tolerance`: The required error tolerance.
- `iterations`: The number of LU decompositions to perform.
- `steps`: A deprecated name for `iterations`.
For large systems a number of iterations is generally required.
"""
iterations = min(iterations, maxIterations)
PysparseSolver.__init__(self, tolerance = tolerance, iterations=iterations, steps = steps, precon = precon)
def __init__(self, *args, **kwargs):
import warnings
warnings.warn("The PySparse CGS solver may return incorrect results for some matrices", UserWarning)
PysparseSolver.__init__(self, *args, **kwargs)
