def simulate_chain(in_prob, affine, **solve_kwargs):
# get a ParamConeProg object
reductions = [Dcp2Cone(), CvxAttr2Constr(), ConeMatrixStuffing()]
chain = Chain(None, reductions)
cone_prog, inv_prob2cone = chain.apply(in_prob)
# apply the Slacks reduction, reconstruct a high-level problem,
# solve the problem, invert the reduction.
cone_prog = ConicSolver().format_constraints(cone_prog,
exp_cone_order=[0, 1, 2])
data, inv_data = a2d.Slacks.apply(cone_prog, affine)
G, h, f, K_dir, K_aff = data[s.A], data[s.B], data[
s.C], data['K_dir'], data['K_aff']
G = sp.sparse.csc_matrix(G)
y = cp.Variable(shape=(G.shape[1], ))
objective = cp.Minimize(f @ y)
aff_con = TestSlacks.set_affine_constraints(G, h, y, K_aff)
dir_con = TestSlacks.set_direct_constraints(y, K_dir)
int_con = TestSlacks.set_integer_constraints(y, data)
constraints = aff_con + dir_con + int_con
slack_prob = cp.Problem(objective, constraints)
slack_prob.solve(**solve_kwargs)
slack_prims = {
a2d.FREE: y[:cone_prog.x.size].value
} # nothing else need be populated.
slack_sol = cp.Solution(slack_prob.status, slack_prob.value,
slack_prims, None, dict())
cone_sol = a2d.Slacks.invert(slack_sol, inv_data)
# pass solution up the solving chain
in_prob_sol = chain.invert(cone_sol, inv_prob2cone)
in_prob.unpack(in_prob_sol)
def simulate_chain(in_prob):
# Get a ParamConeProg object
reductions = [Dcp2Cone(), CvxAttr2Constr(), ConeMatrixStuffing()]
chain = Chain(None, reductions)
cone_prog, inv_prob2cone = chain.apply(in_prob)
# Dualize the problem, reconstruct a high-level cvxpy problem for the dual.
# Solve the problem, invert the dualize reduction.
solver = ConicSolver()
cone_prog = solver.format_constraints(cone_prog,
exp_cone_order=[0, 1, 2])
data, inv_data = a2d.Dualize.apply(cone_prog)
A, b, c, K_dir = data[s.A], data[s.B], data[s.C], data['K_dir']
y = cp.Variable(shape=(A.shape[1], ))
constraints = [A @ y == b]
i = K_dir[a2d.FREE]
dual_prims = {a2d.FREE: y[:i], a2d.SOC: []}
if K_dir[a2d.NONNEG]:
dim = K_dir[a2d.NONNEG]
dual_prims[a2d.NONNEG] = y[i:i + dim]
constraints.append(y[i:i + dim] >= 0)
i += dim
for dim in K_dir[a2d.SOC]:
dual_prims[a2d.SOC].append(y[i:i + dim])
constraints.append(SOC(y[i], y[i + 1:i + dim]))
i += dim
if K_dir[a2d.DUAL_EXP]:
dual_prims[a2d.DUAL_EXP] = y[i:]
y_de = cp.reshape(y[i:], ((y.size - i) // 3, 3),
order='C') # fill rows first
constraints.append(
ExpCone(-y_de[:, 1], -y_de[:, 0],
np.exp(1) * y_de[:, 2]))
objective = cp.Maximize(c @ y)
dual_prob = cp.Problem(objective, constraints)
dual_prob.solve(solver='SCS', eps=1e-8)
dual_prims[a2d.FREE] = dual_prims[a2d.FREE].value
if K_dir[a2d.NONNEG]:
dual_prims[a2d.NONNEG] = dual_prims[a2d.NONNEG].value
dual_prims[a2d.SOC] = [expr.value for expr in dual_prims[a2d.SOC]]
if K_dir[a2d.DUAL_EXP]:
dual_prims[a2d.DUAL_EXP] = dual_prims[a2d.DUAL_EXP].value
dual_duals = {s.EQ_DUAL: constraints[0].dual_value}
dual_sol = cp.Solution(dual_prob.status, dual_prob.value, dual_prims,
dual_duals, dict())
cone_sol = a2d.Dualize.invert(dual_sol, inv_data)
# Pass the solution back up the solving chain.
in_prob_sol = chain.invert(cone_sol, inv_prob2cone)
in_prob.unpack(in_prob_sol)
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 _solve(self,
solver=None,
warm_start=True,
verbose=False,
gp=False,
qcp=False,
requires_grad=False,
enforce_dpp=False,
**kwargs):
"""Solves a DCP compliant optimization problem.
Saves the values of primal and dual variables in the variable
and constraint objects, respectively.
Arguments
---------
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.
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.
requires_grad : bool, optional
Makes it possible to compute gradients with respect to
parameters by calling `backward()` after solving, or to compute
perturbations to the variables by calling `derivative()`. When
True, the solver must be SCS, and dqcp must be False.
A DPPError is thrown when problem is not DPP.
enforce_dpp : bool, optional
When True, a DPPError will be thrown when trying to solve a non-DPP
problem (instead of just a warning). Defaults to False.
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.
"""
for parameter in self.parameters():
if parameter.value is None:
raise error.ParameterError(
"A Parameter (whose name is '%s') does not have a value "
"associated with it; all Parameter objects must have "
"values before solving a problem." % parameter.name())
if requires_grad:
dpp_context = 'dgp' if gp else 'dcp'
if qcp:
raise ValueError("Cannot compute gradients of DQCP problems.")
elif not self.is_dpp(dpp_context):
raise error.DPPError("Problem is not DPP (when requires_grad "
"is True, problem must be DPP).")
elif solver is not None and solver not in [s.SCS, s.DIFFCP]:
raise ValueError("When requires_grad is True, the only "
"supported solver is SCS "
"(received %s)." % solver)
elif s.DIFFCP not in slv_def.INSTALLED_SOLVERS:
raise ImportError(
"The Python package diffcp must be installed to "
"differentiate through problems. Please follow the "
"installation instructions at "
"https://github.com/cvxgrp/diffcp")
else:
solver = s.DIFFCP
else:
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
data, solving_chain, inverse_data = self.get_problem_data(
solver, gp, enforce_dpp)
solution = solving_chain.solve_via_data(self, data, warm_start,
verbose, kwargs)
self.unpack_results(solution, 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
