def initialize(**kwds):
obj = Options(**kwds)
#
# Set obj.available
#
opt = None
try:
opt = SolverFactory(obj.name, solver_io=obj.io)
except:
pass
if opt is None or isinstance(opt, UnknownSolver):
obj.available = False
elif (obj.name == "gurobi") and (not GUROBISHELL.license_is_valid()):
obj.available = False
elif (obj.name == "baron") and (not BARONSHELL.license_is_valid()):
obj.available = False
else:
obj.available = (opt.available(exception_flag=False)) and (
(not hasattr(opt, "executable")) or (opt.executable() is not None)
)
#
# Check capabilities
#
if obj.available:
for _c in obj.capabilities:
if not _c in opt._capabilities:
raise ValueError("Solver %s does not support capability %s!" % (obj.name, _c))
#
# Get version
#
obj.version = opt.version()
return obj
def initialize(**kwds):
obj = Options(**kwds)
#
# Set obj.available
#
opt = None
try:
opt = SolverFactory(obj.name, solver_io=obj.io)
except:
pass
if opt is None or isinstance(opt, UnknownSolver):
obj.available = False
elif (obj.name == "gurobi") and \
(not GUROBISHELL.license_is_valid()):
obj.available = False
elif (obj.name == "baron") and \
(not BARONSHELL.license_is_valid()):
obj.available = False
else:
obj.available = \
(opt.available(exception_flag=False)) and \
((not hasattr(opt,'executable')) or \
(opt.executable() is not None))
#
# Check capabilities
#
if obj.available:
for _c in obj.capabilities:
if not _c in opt._capabilities:
raise ValueError("Solver %s does not support capability %s!" % (obj.name, _c))
#
# Get version
#
obj.version = opt.version()
return obj
def initialize(**kwds):
obj = Options(**kwds)
#
# Set the limits for the solver's "demo" (unlicensed) mode:
# ( nVars, nCons, nNonZeros )
obj.demo_limits = (None, None, None)
if (obj.name == "baron") and \
(not BARONSHELL.license_is_valid()):
obj.demo_limits = (10, 10, 50)
#
#
# Set obj.available
#
opt = None
try:
opt = SolverFactory(obj.name, solver_io=obj.io)
except:
pass
if opt is None or isinstance(opt, UnknownSolver):
obj.available = False
elif (obj.name == "gurobi") and \
(not GUROBISHELL.license_is_valid()):
obj.available = False
elif (obj.name == "mosek") and \
(not MosekDirect.license_is_valid()):
obj.available = False
else:
obj.available = \
(opt.available(exception_flag=False)) and \
((not hasattr(opt,'executable')) or \
(opt.executable() is not None))
#
# Check capabilities, even if the solver is not available
#
if not (opt is None or isinstance(opt, UnknownSolver)):
for _c in obj.capabilities:
if not _c in opt._capabilities:
raise ValueError("Solver %s does not support capability %s!" %
(obj.name, _c))
#
# Get version
#
if obj.available:
obj.version = opt.version()
return obj
def initialize(**kwds):
obj = Bunch(**kwds)
#
# Set obj.available
#
try:
opt = SolverFactory(obj.name, solver_io=obj.io)
except:
opt = None
if opt is None or isinstance(opt, UnknownSolver):
obj.available = False
elif not opt.available(exception_flag=False):
obj.available = False
elif hasattr(opt, 'executable') and opt.executable() is None:
obj.available = False
elif not opt.license_is_valid() \
and obj.name not in licensed_solvers_with_demo_mode:
obj.available = False
else:
obj.available = True
#
# Set the limits for the solver's "demo" (unlicensed) mode:
# ( nVars, nCons, nNonZeros )
obj.demo_limits = (None, None, None)
if obj.available:
if obj.name == "baron" and not opt.license_is_valid():
obj.demo_limits = (10, 10, 50)
#
# Check capabilities, even if the solver is not available
#
if not (opt is None or isinstance(opt, UnknownSolver)):
for _c in obj.capabilities:
if not _c in opt._capabilities:
raise ValueError("Solver %s does not support capability %s!" %
(obj.name, _c))
#
# Get version
#
if obj.available:
obj.version = opt.version()
return obj
def denoiseopt(dnmodel,
mean_lbound=0,
mean_ubound=0,
g2_lbound=0,
g2_ubound=0,
eps_tol=0,
solver_name='ipopt',
solver_path='',
save_all_nondom_x=False,
plot=False,
save_all_nondom_y=False,
disp=False):
"""
Interface to epsilon-constrained algorithm
Made to denoise photon-number or photounting statistics
from the coherent background.
It is usable for recovering spectrum of fluctuations, excess noise or
single-photon signal.
Parameters
----------
dnmodel : qpip.denoise.DenoisePBaseModel
A model of the problem to solve.
mean_lbound : float, optional
Minimal possible value of noise's mean. The default is 0 and ignored.
Also ignored if g2 bounds exist.
mean_ubound : float, optional
Maximal possible value of noise's mean. The default is 0 and ignored.
Also ignored if g2 bounds exist.
g2_lbound : float, optional
Minimal possible value of noise's g2. The default is 0 and ignored.
Also ignored if mean bounds exist.
g2_ubound : float, optional
Maximal possible value of noise's g2. The default is 0 and ignored.
Also ignored if mean bounds exist.
solver_name : str, optional
Solver to solve the problem.
The default is ipopt if installed.
solver_path : str, optional
Path to the solver executable
eps_tol : float, optional
Tolarance of additional variables to finish iterations.
The default is 0.
save_all_nondom_x : bool, optional
Save all nondominate solutions in output.
The default is False.
save_all_nondom_y : bool, optional
Save y vector values for all nondominate solutions.
The default is False.
disp : bool, optional
Display calculation progress.
Messages have following format:
'iteration number' 'status (1 is success, 0 is fail)' ['discrepancy', 'noise_mean', 'negentropy', 'noise_g2']
The default is False.
plot : bool, optional
Plot dependency of vars from an iteration number
after the end of calculations.
The default is False.
Returns
-------
res : scipy.optimize.OptimizeResult
see help(qpip.epscon.iterate).
"""
if g2(dnmodel.P) < 1:
warn(
'g2 of the given distribution is less than 1. Algorithm can not use g2 and mean bounds.',
RuntimeWarning, __file__, 171)
solver = SolverFactory(solver_name, executable=solver_path, solver_io="nl")
if solver_path:
solver.executable = solver_path
y_vars = ['discrepancy', 'noise_mean', 'negentropy', 'noise_g2']
x_var = 'p_noise'
if mean_ubound * g2_ubound or mean_ubound * g2_lbound or mean_lbound * g2_ubound or mean_lbound * g2_lbound:
mean_ubound = 0
mean_lbound = 0
warn(
'It is not possible to set g2 and mean bounds together. Mean bounds are ignored.',
RuntimeWarning, __file__, 101)
if mean_ubound:
dnmodel.c_low_g2 = Constraint(
expr=dnmodel.noise_g2 >= noiseg2_by_mean(dnmodel.P, mean_ubound))
dnmodel.c_top_mean = Constraint(expr=dnmodel.noise_mean <= mean_ubound)
if mean_lbound:
dnmodel.c_top_g2 = Constraint(
expr=dnmodel.noise_g2 <= noiseg2_by_mean(dnmodel.P, mean_lbound))
dnmodel.c_low_mean = Constraint(expr=dnmodel.noise_mean >= mean_lbound)
if g2_ubound:
dnmodel.c_top_g2 = Constraint(expr=dnmodel.noise_g2 <= g2_ubound)
dnmodel.c_low_mean = Constraint(
expr=dnmodel.noise_mean >= noisemean_by_g2(dnmodel.P, g2_ubound))
if g2_lbound:
dnmodel.c_low_g2 = Constraint(expr=dnmodel.noise_g2 >= g2_lbound)
dnmodel.c_top_mean = Constraint(
expr=dnmodel.noise_mean <= noisemean_by_g2(dnmodel.P, g2_lbound))
mean_bound = mean(dnmodel.P) if not mean_ubound else mean_ubound
noise_bounds = [[
max(0, mean_lbound), -np.log(len(dnmodel.NSET)),
max(1, g2_lbound)
], [mean_bound, 0, max(20, g2_ubound)]]
res = iterate(dnmodel,
solver,
y_vars,
x_var,
eps_bounds=noise_bounds,
eps_tol=eps_tol,
save_all_nondom_x=save_all_nondom_x,
save_all_nondom_y=save_all_nondom_y,
disp=disp)
info('Optimization ended in %d iterations with eps_tol %.1e' %
(res.nit, eps_tol))
info('Status "{0}", "{1}"'.format(*res.status))
if plot and res.nit > 1:
for i in lrange(y_vars):
plt.plot([e[i] for e in res.ndy], label=y_vars[i])
plt.legend(frameon=False)
plt.show()
return res
