def construct_intermediate_chain(problem, candidates, gp: bool = False):
"""
Builds a chain that rewrites a problem into an intermediate
representation suitable for numeric reductions.
Parameters
----------
problem : Problem
The problem for which to build a chain.
candidates : dict
Dictionary of candidate solvers divided in qp_solvers
and conic_solvers.
gp : bool
If True, the problem is parsed as a Disciplined Geometric Program
instead of as a Disciplined Convex Program.
Returns
-------
Chain
A Chain that can be used to convert the problem to an intermediate form.
Raises
------
DCPError
Raised if the problem is not DCP and `gp` is False.
DGPError
Raised if the problem is not DGP and `gp` is True.
"""
reductions = []
if len(problem.variables()) == 0:
return Chain(reductions=reductions)
# TODO Handle boolean constraints.
if complex2real.accepts(problem):
reductions += [complex2real.Complex2Real()]
if gp:
reductions += [Dgp2Dcp()]
if not gp and not problem.is_dcp():
append = build_non_disciplined_error_msg(problem, 'DCP')
if problem.is_dgp():
append += ("\nHowever, the problem does follow DGP rules. "
"Consider calling solve() with `gp=True`.")
elif problem.is_dqcp():
append += ("\nHowever, the problem does follow DQCP rules. "
"Consider calling solve() with `qcp=True`.")
raise DCPError("Problem does not follow DCP rules. Specifically:\n" +
append)
elif gp and not problem.is_dgp():
append = build_non_disciplined_error_msg(problem, 'DGP')
if problem.is_dcp():
append += ("\nHowever, the problem does follow DCP rules. "
"Consider calling solve() with `gp=False`.")
elif problem.is_dqcp():
append += ("\nHowever, the problem does follow DQCP rules. "
"Consider calling solve() with `qcp=True`.")
raise DGPError("Problem does not follow DGP rules." + append)
# Dcp2Cone and Qp2SymbolicQp require problems to minimize their objectives.
if type(problem.objective) == Maximize:
reductions += [FlipObjective()]
# First, attempt to canonicalize the problem to a linearly constrained QP.
if candidates['qp_solvers'] and qp2symbolic_qp.accepts(problem):
reductions += [CvxAttr2Constr(), Qp2SymbolicQp()]
return Chain(reductions=reductions)
# Canonicalize it to conic problem.
if not candidates['conic_solvers']:
raise SolverError("Problem could not be reduced to a QP, and no "
"conic solvers exist among candidate solvers "
"(%s)." % candidates)
reductions += [Dcp2Cone(), CvxAttr2Constr()]
return Chain(reductions=reductions)
def _reductions_for_problem_class(problem,
candidates,
gp: bool = False) -> List[Any]:
"""
Builds a chain that rewrites a problem into an intermediate
representation suitable for numeric reductions.
Parameters
----------
problem : Problem
The problem for which to build a chain.
candidates : dict
Dictionary of candidate solvers divided in qp_solvers
and conic_solvers.
gp : bool
If True, the problem is parsed as a Disciplined Geometric Program
instead of as a Disciplined Convex Program.
Returns
-------
list of Reduction objects
A list of reductions that can be used to convert the problem to an
intermediate form.
Raises
------
DCPError
Raised if the problem is not DCP and `gp` is False.
DGPError
Raised if the problem is not DGP and `gp` is True.
"""
reductions = []
# TODO Handle boolean constraints.
if complex2real.accepts(problem):
reductions += [complex2real.Complex2Real()]
if gp:
reductions += [Dgp2Dcp()]
if not gp and not problem.is_dcp():
append = build_non_disciplined_error_msg(problem, 'DCP')
if problem.is_dgp():
append += ("\nHowever, the problem does follow DGP rules. "
"Consider calling solve() with `gp=True`.")
elif problem.is_dqcp():
append += ("\nHowever, the problem does follow DQCP rules. "
"Consider calling solve() with `qcp=True`.")
raise DCPError("Problem does not follow DCP rules. Specifically:\n" +
append)
elif gp and not problem.is_dgp():
append = build_non_disciplined_error_msg(problem, 'DGP')
if problem.is_dcp():
append += ("\nHowever, the problem does follow DCP rules. "
"Consider calling solve() with `gp=False`.")
elif problem.is_dqcp():
append += ("\nHowever, the problem does follow DQCP rules. "
"Consider calling solve() with `qcp=True`.")
raise DGPError("Problem does not follow DGP rules." + append)
# Dcp2Cone and Qp2SymbolicQp require problems to minimize their objectives.
if type(problem.objective) == Maximize:
reductions += [FlipObjective()]
if _solve_as_qp(problem, candidates):
reductions += [CvxAttr2Constr(), qp2symbolic_qp.Qp2SymbolicQp()]
else:
# Canonicalize it to conic problem.
if not candidates['conic_solvers']:
raise SolverError("Problem could not be reduced to a QP, and no "
"conic solvers exist among candidate solvers "
"(%s)." % candidates)
else:
reductions += [Dcp2Cone(), CvxAttr2Constr()]
constr_types = {type(c) for c in problem.constraints}
if FiniteSet in constr_types:
reductions += [Valinvec2mixedint()]
return reductions
