def parallel_penalty_one_iteration_solve(self,N,m,my_list,x0=None,
Lbfgs_options=None,
algorithm='my_lbfgs',scale=False):
dt=float(self.T)/N
if x0==None:
x0 = np.zeros(N+m)
x = None
if algorithm=='my_lbfgs':
x0 = self.Vec(x0)
Result = []
rank = self.comm.Get_rank()
for i in range(len(my_list)):
def J(u):
return self.parallel_penalty_functional(u,N,my_list[i])
def grad_J(u):
return self.penalty_grad(u,N,m,my_list[i])
if algorithm=='my_lbfgs':
self.update_Lbfgs_options(Lbfgs_options)
if scale:
scaler={'m':m,'factor':self.Lbfgs_options['scale_factor']}
solver = Lbfgs(J,grad_J,x0,options=self.Lbfgs_options,
scale=scaler)
else:
solver = Lbfgs(J,grad_J,x0,options=self.Lbfgs_options)
res = solver.one_iteration(self.comm)
Result.append(res)
#print res.array(),rank
#x0 = res['control']
#print J(x0.array())
if rank ==0:
Result.append(res)
else:
Result.append(0)
return Result
if len(Result)==1:
return res
else:
return Result
