def _solve(self,
solver=None,
warm_start=True,
verbose=False,
parallel=False,
gp=False,
**kwargs):
"""Solves a DCP compliant optimization problem.
Saves the values of primal and dual variables in the variable
and constraint objects, respectively.
Parameters
----------
solver : str, optional
The solver to use. Defaults to ECOS.
warm_start : bool, optional
Should the previous solver result be used to warm start?
verbose : bool, optional
Overrides the default of hiding solver output.
parallel : bool, optional
If problem is separable, solve in parallel.
gp : bool, optional
If True, then parses the problem as a disciplined geometric program
instead of a disciplined convex program.
kwargs : dict, optional
A dict of options that will be passed to the specific solver.
In general, these options will override any default settings
imposed by cvxpy.
Returns
-------
float
The optimal value for the problem, or a string indicating
why the problem could not be solved.
"""
if parallel:
from cvxpy.transforms.separable_problems import get_separable_problems
self._separable_problems = (get_separable_problems(self))
if len(self._separable_problems) > 1:
return self._parallel_solve(solver, warm_start, verbose,
**kwargs)
self._construct_chains(solver=solver, gp=gp)
data, solving_inverse_data = \
self._solving_chain.apply(self._intermediate_problem)
solution = self._solving_chain.solve_via_data(self, data, warm_start,
verbose, kwargs)
full_chain = \
self._solving_chain.prepend(self._intermediate_chain)
inverse_data = self._intermediate_inverse_data + solving_inverse_data
self.unpack_results(solution, full_chain, inverse_data)
return self.value
def _solve(self,
solver=None,
ignore_dcp=False,
warm_start=True,
verbose=False,
parallel=False,
gp=False,
**kwargs):
"""Solves a DCP compliant optimization problem.
Saves the values of primal and dual variables in the variable
and constraint objects, respectively.
Parameters
----------
solver : str, optional
The solver to use. Defaults to ECOS.
ignore_dcp : bool, optional
TODO(akshayka): This option will probably be eliminated.
Overrides the default of raising an exception if the problem is not
DCP.
warm_start : bool, optional
Should the previous solver result be used to warm start?
verbose : bool, optional
Overrides the default of hiding solver output.
parallel : bool, optional
If problem is separable, solve in parallel.
gp : bool, optional
If True, then parses the problem as a disciplined geometric program
instead of a disciplined convex program.
kwargs : dict, optional
A dict of options that will be passed to the specific solver.
In general, these options will override any default settings
imposed by cvxpy.
Returns
-------
float
The optimal value for the problem, or a string indicating
why the problem could not be solved.
"""
if parallel:
from cvxpy.transforms.separable_problems import get_separable_problems
self._separable_problems = (get_separable_problems(self))
if len(self._separable_problems) > 1:
return self._parallel_solve(solver, ignore_dcp, warm_start,
verbose, **kwargs)
chain_key = (solver, gp)
if chain_key != self._cached_chain_key:
try:
self._solving_chain = construct_solving_chain(self,
solver=solver,
gp=gp)
except Exception as e:
raise e
self._cached_chain_key = chain_key
data, inverse_data = self._solving_chain.apply(self)
solver_output = self._solving_chain.solve_via_data(
self, data, warm_start, verbose, kwargs)
self.unpack_results(solver_output, self._solving_chain, inverse_data)
return self.value
def _solve(self,
solver=None,
ignore_dcp=False,
warm_start=True,
verbose=False,
parallel=False,
**kwargs):
"""Solves a DCP compliant optimization problem.
Saves the values of primal and dual variables in the variable
and constraint objects, respectively.
Parameters
----------
solver : str, optional
The solver to use. Defaults to ECOS.
ignore_dcp : bool, optional
TODO(akshayka): This option will probably be eliminated.
Overrides the default of raising an exception if the problem is not
DCP.
warm_start : bool, optional
Should the previous solver result be used to warm start?
verbose : bool, optional
Overrides the default of hiding solver output.
parallel : bool, optional
If problem is separable, solve in parallel.
kwargs : dict, optional
A dict of options that will be passed to the specific solver.
In general, these options will override any default settings
imposed by cvxpy.
Returns
-------
float
The optimal value for the problem, or a string indicating
why the problem could not be solved.
"""
if parallel:
from cvxpy.transforms.separable_problems import get_separable_problems
self._separable_problems = (get_separable_problems(self))
if len(self._separable_problems) > 1:
return self._parallel_solve(solver, ignore_dcp, warm_start,
verbose, **kwargs)
# If a previous chain does not exist, or if the solver is
# specified and it does not match the solver used for the previous
# solve, then construct a new solving chain.
if (self._solving_chain is None
or (solver is not None
and self._solving_chain.solver.name != solver)):
try:
self._solving_chain = construct_solving_chain(self,
solver=solver)
except Exception as e:
raise e
data, inverse_data = self._solving_chain.apply(self)
solution = self._solving_chain.solve_via_data(self, data, warm_start,
verbose, kwargs)
self.unpack_results(solution, self._solving_chain, inverse_data)
return self.value
def _solve(self,
solver=None,
warm_start=True,
verbose=False,
parallel=False,
gp=False,
qcp=False,
**kwargs):
"""Solves a DCP compliant optimization problem.
Saves the values of primal and dual variables in the variable
and constraint objects, respectively.
Parameters
----------
solver : str, optional
The solver to use. Defaults to ECOS.
warm_start : bool, optional
Should the previous solver result be used to warm start?
verbose : bool, optional
Overrides the default of hiding solver output.
parallel : bool, optional
If problem is separable, solve in parallel.
gp : bool, optional
If True, parses the problem as a disciplined geometric program.
qcp : bool, optional
If True, parses the problem as a disciplined quasiconvex program.
kwargs : dict, optional
A dict of options that will be passed to the specific solver.
In general, these options will override any default settings
imposed by cvxpy.
Returns
-------
float
The optimal value for the problem, or a string indicating
why the problem could not be solved.
"""
if gp and qcp:
raise ValueError("At most one of `gp` and `qcp` can be True.")
if qcp and not self.is_dcp():
if not self.is_dqcp():
raise error.DQCPError("The problem is not DQCP.")
reductions = [dqcp2dcp.Dqcp2Dcp()]
if type(self.objective) == Maximize:
reductions = [FlipObjective()] + reductions
chain = Chain(problem=self, reductions=reductions)
soln = bisection.bisect(chain.reduce(),
solver=solver,
verbose=verbose,
**kwargs)
self.unpack(chain.retrieve(soln))
return self.value
if parallel:
from cvxpy.transforms.separable_problems import get_separable_problems
self._separable_problems = (get_separable_problems(self))
if len(self._separable_problems) > 1:
return self._parallel_solve(solver, warm_start, verbose,
**kwargs)
self._construct_chains(solver=solver, gp=gp)
data, solving_inverse_data = self._solving_chain.apply(
self._intermediate_problem)
solution = self._solving_chain.solve_via_data(self, data, warm_start,
verbose, kwargs)
full_chain = self._solving_chain.prepend(self._intermediate_chain)
inverse_data = self._intermediate_inverse_data + solving_inverse_data
self.unpack_results(solution, full_chain, inverse_data)
return self.value
