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 test_psd_constraints(self):
""" Test positive semi-definite constraints
"""
C = Variable((3, 3))
obj = Maximize(C[0, 2])
constraints = [
diag(C) == 1, C[0, 1] == 0.6, C[1, 2] == -0.3, C == C.T, C >> 0
]
prob = Problem(obj, constraints)
self.assertTrue(FlipObjective().accepts(prob))
p_min = FlipObjective().apply(prob)
self.assertTrue(ConeMatrixStuffing().accepts(p_min[0]))
C = Variable((2, 2))
obj = Maximize(C[0, 1])
constraints = [C == 1, C >> [[2, 0], [0, 2]]]
prob = Problem(obj, constraints)
self.assertTrue(FlipObjective().accepts(prob))
p_min = FlipObjective().apply(prob)
self.assertTrue(ConeMatrixStuffing().accepts(p_min[0]))
C = Variable((2, 2), symmetric=True)
obj = Minimize(C[0, 0])
constraints = [C << [[2, 0], [0, 2]]]
prob, _ = CvxAttr2Constr().apply(Problem(obj, constraints))
self.assertTrue(ConeMatrixStuffing().accepts(prob))
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 _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
