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)
