Source code for cvxpy.reductions.chain

from cvxpy import settings as s
from cvxpy.reductions.reduction import Reduction

[docs]class Chain(Reduction): """A logical grouping of multiple reductions into a single reduction. Attributes ---------- reductions : list[Reduction] A list of reductions. """ def __init__(self, problem=None, reductions=None) -> None: super(Chain, self).__init__(problem=problem) self.reductions = [] if reductions is None else reductions def __str__(self): return str(self.reductions) def __repr__(self) -> str: return "Chain(reductions=%s)" % repr(self.reductions) def get(self, reduction_type): for reduction in self.reductions: if isinstance(reduction, reduction_type): return reduction raise KeyError
[docs] def accepts(self, problem) -> bool: """A problem is accepted if the sequence of reductions is valid. In particular, the i-th reduction must accept the output of the i-1th reduction, with the first reduction (self.reductions[0]) in the sequence taking as input the supplied problem. Parameters ---------- problem : Problem The problem to check. Returns ------- bool True if the chain can be applied, False otherwise. """ for r in self.reductions: if not r.accepts(problem): return False problem, _ = r.apply(problem) return True
[docs] def apply(self, problem, verbose: bool = False): """Applies the chain to a problem and returns an equivalent problem. Parameters ---------- problem : Problem The problem to which the chain will be applied. verbose : bool, optional Whehter to print verbose output. Returns ------- Problem or dict The problem yielded by applying the reductions in sequence, starting at self.reductions[0]. list The inverse data yielded by each of the reductions. """ inverse_data = [] for r in self.reductions: if verbose:'Applying reduction %s', type(r).__name__) problem, inv = r.apply(problem) inverse_data.append(inv) return problem, inverse_data
[docs] def invert(self, solution, inverse_data): """Returns a solution to the original problem given the inverse_data. """ for r, inv in reversed(list(zip(self.reductions, inverse_data))): solution = r.invert(solution, inv) return solution