def minimize_scipy_generic(rf_np, method, bounds = None, **kwargs):
''' Interface to the generic minimize method in scipy '''
try:
from scipy.optimize import minimize as scipy_minimize
except ImportError:
print "**************** Deprecated warning *****************"
print "You have an unusable installation of scipy. This version is not supported by dolfin-adjoint."
try:
import scipy
print "Version: %s\tFile: %s" % (scipy.__version__, scipy.__file__)
except:
pass
raise
if method in ["Newton-CG"]:
forget = None
else:
forget = False
project = kwargs.pop("project", False)
m = [p.data() for p in rf_np.controls]
m_global = rf_np.obj_to_array(m)
J = rf_np.__call__
dJ = lambda m: rf_np.derivative(m, forget=forget, project=project)
H = rf_np.hessian
if not "options" in kwargs:
kwargs["options"] = {}
if rank(rf_np.rf.mpi_comm()) != 0:
# Shut up all processors except the first one.
kwargs["options"]["disp"] = False
else:
# Print out progress information by default
if not "disp" in kwargs["options"]:
kwargs["options"]["disp"] = True
# Make the default SLSLQP options more verbose
if method == "SLSQP" and "iprint" not in kwargs["options"]:
kwargs["options"]["iprint"] = 2
# For gradient-based methods add the derivative function to the argument list
if method not in ["COBYLA", "Nelder-Mead", "Anneal", "Powell"]:
kwargs["jac"] = dJ
# For Hessian-based methods add the Hessian action function to the argument list
if method in ["Newton-CG"]:
kwargs["hessp"] = H
if "constraints" in kwargs:
from constraints import canonicalise, InequalityConstraint, EqualityConstraint
constraints = canonicalise(kwargs["constraints"])
scipy_c = []
for c in constraints:
if isinstance(c, InequalityConstraint):
typestr = "ineq"
elif isinstance(c, EqualityConstraint):
typestr = "eq"
else:
raise Exception, "Unknown constraint class"
def jac(x):
out = c.jacobian(x)
return [gather(y) for y in out]
scipy_c.append(dict(type=typestr, fun=c.function, jac=jac))
kwargs["constraints"] = scipy_c
if method=="basinhopping":
try:
from scipy.optimize import basinhopping
except ImportError:
print "**************** Outdated scipy version warning *****************"
print "The basin hopping optimisation algorithm requires scipy >= 0.12."
raise ImportError
del kwargs["options"]
del kwargs["jac"]
kwargs["minimizer_kwargs"]["jac"]=dJ
if "bounds" in kwargs["minimizer_kwargs"]:
kwargs["minimizer_kwargs"]["bounds"] = \
serialise_bounds(rf_np, kwargs["minimizer_kwargs"]["bounds"])
res = basinhopping(J, m_global, **kwargs)
elif bounds != None:
bounds = serialise_bounds(rf_np, bounds)
res = scipy_minimize(J, m_global, method=method, bounds=bounds, **kwargs)
else:
res = scipy_minimize(J, m_global, method=method, **kwargs)
rf_np.set_controls(np.array(res["x"]))
m = [p.data() for p in rf_np.controls]
return m