def __init__(self, g, H, **kwargs): TruncatedCG.__init__(self, g, H, **kwargs) self.name = 'Suff-CG' self.qval = 0.0 # Initial value of quadratic objective. self.best_decrease = 0 self.cg_reltol = kwargs.get('cg_reltol', 0.1) self.detect_stalling = kwargs.get('detect_stalling', True)
def __init__(self, J, c, radius=None, transposed=False, **kwargs): """ :parameters: :J: coefficient matrix (may be rectangular) :c: constant vector (numpy array) :radius: positive real number or None (default: None) :transpose: if set to True, replace J with its transpose. `J` should be a linear operator. Additional keyword arguments are passed directly to `TruncatedCG`. Upon completion, the member `step` is set to the most recent solution estimate. See the documentation of `TruncatedCG` for more information. """ self.op = SquaredLinearOperator(J, transposed=transposed) TruncatedCG.__init__(self, J.T * c, self.op, radius=radius, **kwargs) return