def op_runner(objective, constraints, **kwargs):
"""
Minimize the objective specified by the constraints.
This safely let's you pass options to the solver and restores their values
once the optimization process has completed.
The objective must be linear in the variables.
This uses cvxopt.modeling.
"""
from cvxopt.solvers import options
from cvxopt.modeling import op
old_options = options.copy()
opt = op(objective, constraints)
try:
options.clear()
options.update(kwargs)
# Ignore 0 log 0 warnings.
with np.errstate(divide='ignore', invalid='ignore'):
opt.solve()
except:
raise
finally:
options.clear()
options.update(old_options)
return opt
def op_runner(objective, constraints, **kwargs):
"""
Minimize the objective specified by the constraints.
This safely let's you pass options to the solver and restores their values
once the optimization process has completed.
The objective must be linear in the variables.
This uses cvxopt.modeling.
"""
from cvxopt.solvers import options
from cvxopt.modeling import variable, op
old_options = options.copy()
opt = op(objective, constraints)
try:
options.clear()
options.update(kwargs)
# Ignore 0 log 0 warnings.
with np.errstate(divide='ignore', invalid='ignore'):
opt.solve()
except:
raise
finally:
options.clear()
options.update(old_options)
return opt
def optimize(self, **kwargs):
"""
Options:
show_progress=False,
maxiters=100,
abstol=1e-7,
reltol=1e-6,
feastol=1e-7,
refinement=0 if m=0 else 1
"""
from cvxopt.solvers import cp, options
old_options = options.copy()
out = None
try:
options.clear()
options.update(kwargs)
with np.errstate(divide='ignore', invalid='ignore'):
result = cp(F=self.F,
G=self.G,
h=self.h,
dims={
'l': self.G.size[0],
'q': [],
's': []
},
A=self.A,
b=self.b)
except:
raise
else:
self.result = result
out = np.asarray(result['x'])
finally:
options.clear()
options.update(old_options)
return out
def optimize(self, **kwargs):
"""
Options:
show_progress=False,
maxiters=100,
abstol=1e-7,
reltol=1e-6,
feastol=1e-7,
refinement=0 if m=0 else 1
"""
from cvxopt.solvers import cp, options
old_options = options.copy()
out = None
try:
options.clear()
options.update(kwargs)
with np.errstate(divide='ignore', invalid='ignore'):
result = cp(F=self.F,
G=self.G,
h=self.h,
dims={'l':self.G.size[0], 'q':[], 's':[]},
A=self.A,
b=self.b)
except:
raise
else:
self.result = result
out = np.asarray(result['x'])
finally:
options.clear()
options.update(old_options)
return out
