# Source code for cvxpy.reductions.solvers.solving_chain

```
import warnings
from cvxpy.atoms import EXP_ATOMS, PSD_ATOMS, SOC_ATOMS, NONPOS_ATOMS
from cvxpy.constraints import ExpCone, PSD, SOC, NonNeg, \
NonPos, Inequality, Equality, Zero, PowCone3D
from cvxpy.error import DCPError, DGPError, DPPError, SolverError
from cvxpy.problems.objective import Maximize
from cvxpy.reductions.chain import Chain
from cvxpy.reductions.complex2real import complex2real
from cvxpy.reductions.cvx_attr2constr import CvxAttr2Constr
from cvxpy.reductions.dcp2cone.cone_matrix_stuffing import ConeMatrixStuffing
from cvxpy.reductions.dcp2cone.dcp2cone import Dcp2Cone
from cvxpy.reductions.cone2cone.exotic2common import EXOTIC_CONES, Exotic2Common
from cvxpy.reductions.dgp2dcp.dgp2dcp import Dgp2Dcp
from cvxpy.reductions.eval_params import EvalParams
from cvxpy.reductions.flip_objective import FlipObjective
from cvxpy.reductions.qp2quad_form import qp2symbolic_qp
from cvxpy.reductions.qp2quad_form.qp_matrix_stuffing import QpMatrixStuffing
from cvxpy.reductions.solvers.constant_solver import ConstantSolver
from cvxpy.reductions.solvers.solver import Solver
from cvxpy.reductions.solvers import defines as slv_def
from cvxpy.utilities.debug_tools import build_non_disciplined_error_msg
from typing import Any, List
def _is_lp(self):
"""Is problem a linear program?
"""
for c in self.constraints:
if not (isinstance(c, (Equality, Zero)) or c.args[0].is_pwl()):
return False
for var in self.variables():
if var.is_psd() or var.is_nsd():
return False
return (self.is_dcp() and self.objective.args[0].is_pwl())
def _solve_as_qp(problem, candidates):
if _is_lp(problem) and \
[s for s in candidates['conic_solvers'] if s not in candidates['qp_solvers']]:
# OSQP can take many iterations for LPs; use a conic solver instead
# GUROBI and CPLEX QP/LP interfaces are more efficient
# -> Use them instead of conic if applicable.
return False
return candidates['qp_solvers'] and qp2symbolic_qp.accepts(problem)
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()]
return reductions
def construct_solving_chain(problem, candidates,
gp: bool = False,
enforce_dpp: bool = False) -> "SolvingChain":
"""Build a reduction chain from a problem to an installed solver.
Note that if the supplied problem has 0 variables, then the solver
parameter will be ignored.
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.
enforce_dpp : bool, optional
When True, a DPPError will be thrown when trying to parse a non-DPP
problem (instead of just a warning). Defaults to False.
Returns
-------
SolvingChain
A SolvingChain that can be used to solve the problem.
Raises
------
SolverError
Raised if no suitable solver exists among the installed solvers, or
if the target solver is not installed.
"""
if len(problem.variables()) == 0:
return SolvingChain(reductions=[ConstantSolver()])
reductions = _reductions_for_problem_class(problem, candidates, gp)
dpp_context = 'dcp' if not gp else 'dgp'
dpp_error_msg = (
"You are solving a parameterized problem that is not DPP. "
"Because the problem is not DPP, subsequent solves will not be "
"faster than the first one. For more information, see the "
"documentation on Discplined Parametrized Programming, at\n"
"\thttps://www.cvxpy.org/tutorial/advanced/index.html#"
"disciplined-parametrized-programming")
if not problem.is_dpp(dpp_context):
if not enforce_dpp:
warnings.warn(dpp_error_msg)
reductions = [EvalParams()] + reductions
else:
raise DPPError(dpp_error_msg)
elif any(param.is_complex() for param in problem.parameters()):
reductions = [EvalParams()] + reductions
# Conclude with matrix stuffing; choose one of the following paths:
# (1) QpMatrixStuffing --> [a QpSolver],
# (2) ConeMatrixStuffing --> [a ConicSolver]
if _solve_as_qp(problem, candidates):
# Canonicalize as a QP
solver = candidates['qp_solvers'][0]
solver_instance = slv_def.SOLVER_MAP_QP[solver]
reductions += [QpMatrixStuffing(),
solver_instance]
return SolvingChain(reductions=reductions)
# Canonicalize as a cone program
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)
constr_types = set()
# ^ We use constr_types to infer an incomplete list of cones that
# the solver will need after canonicalization.
for c in problem.constraints:
constr_types.add(type(c))
ex_cos = [ct for ct in constr_types if ct in EXOTIC_CONES]
# ^ The way we populate "ex_cos" will need to change if and when
# we have atoms that require exotic cones.
for co in ex_cos:
sim_cos = EXOTIC_CONES[co] # get the set of required simple cones
constr_types.update(sim_cos)
constr_types.remove(co)
# We now go over individual elementary cones support by CVXPY (
# SOC, ExpCone, NonNeg, Zero, PSD, PowCone3D) and check if
# they've appeared in constr_types or if the problem has an atom
# requiring that cone.
cones = []
atoms = problem.atoms()
if SOC in constr_types or any(atom in SOC_ATOMS for atom in atoms):
cones.append(SOC)
if ExpCone in constr_types or any(atom in EXP_ATOMS for atom in atoms):
cones.append(ExpCone)
if any(t in constr_types for t in [Inequality, NonPos, NonNeg]) \
or any(atom in NONPOS_ATOMS for atom in atoms):
cones.append(NonNeg)
if Equality in constr_types or Zero in constr_types:
cones.append(Zero)
if PSD in constr_types \
or any(atom in PSD_ATOMS for atom in atoms) \
or any(v.is_psd() or v.is_nsd() for v in problem.variables()):
cones.append(PSD)
if PowCone3D in constr_types:
# if we add in atoms that specifically use the 3D power cone
# (rather than the ND power cone), then we'll need to check
# for those atoms here as well.
cones.append(PowCone3D)
# Here, we make use of the observation that canonicalization only
# increases the number of constraints in our problem.
has_constr = len(cones) > 0 or len(problem.constraints) > 0
for solver in candidates['conic_solvers']:
solver_instance = slv_def.SOLVER_MAP_CONIC[solver]
if (all(c in solver_instance.SUPPORTED_CONSTRAINTS for c in cones)
and (has_constr or not solver_instance.REQUIRES_CONSTR)):
if ex_cos:
reductions.append(Exotic2Common())
reductions += [ConeMatrixStuffing(), solver_instance]
return SolvingChain(reductions=reductions)
raise SolverError("Either candidate conic solvers (%s) do not support the "
"cones output by the problem (%s), or there are not "
"enough constraints in the problem." % (
candidates['conic_solvers'],
", ".join([cone.__name__ for cone in cones])))
[docs]class SolvingChain(Chain):
"""A reduction chain that ends with a solver.
Parameters
----------
reductions : list[Reduction]
A list of reductions. The last reduction in the list must be a solver
instance.
Attributes
----------
reductions : list[Reduction]
A list of reductions.
solver : Solver
The solver, i.e., reductions[-1].
"""
def __init__(self, problem=None, reductions=None) -> None:
super(SolvingChain, self).__init__(problem=problem,
reductions=reductions)
if not isinstance(self.reductions[-1], Solver):
raise ValueError("Solving chains must terminate with a Solver.")
self.solver = self.reductions[-1]
[docs] def prepend(self, chain) -> "SolvingChain":
"""
Create and return a new SolvingChain by concatenating
chain with this instance.
"""
return SolvingChain(reductions=chain.reductions + self.reductions)
[docs] def solve(self, problem, warm_start, verbose, solver_opts):
"""Solves the problem by applying the chain.
Applies each reduction in the chain to the problem, solves it,
and then inverts the chain to return a solution of the supplied
problem.
Parameters
----------
problem : Problem
The problem to solve.
warm_start : bool
Whether to warm start the solver.
verbose : bool
Whether to enable solver verbosity.
solver_opts : dict
Solver specific options.
Returns
-------
solution : Solution
A solution to the problem.
"""
data, inverse_data = self.apply(problem)
solution = self.solver.solve_via_data(data, warm_start,
verbose, solver_opts)
return self.invert(solution, inverse_data)
[docs] def solve_via_data(self, problem, data, warm_start: bool = False, verbose: bool = False,
solver_opts={}):
"""Solves the problem using the data output by the an apply invocation.
The semantics are:
.. code :: python
data, inverse_data = solving_chain.apply(problem)
solution = solving_chain.invert(solver_chain.solve_via_data(data, ...))
which is equivalent to writing
.. code :: python
solution = solving_chain.solve(problem, ...)
Parameters
----------
problem : Problem
The problem to solve.
data : map
Data for the solver.
warm_start : bool
Whether to warm start the solver.
verbose : bool
Whether to enable solver verbosity.
solver_opts : dict
Solver specific options.
Returns
-------
raw solver solution
The information returned by the solver; this is not necessarily
a Solution object.
"""
return self.solver.solve_via_data(data, warm_start, verbose,
solver_opts, problem._solver_cache)
```