def dc(x):
r = zeros((2, p.n))
r[0,0] = 2 * 4 * x[0]**3
r[1,1] = 2 * x[1]
r[1,2] = 2 * x[2] + 15 #incorrect derivative
return r
p.dc = dc
p.h = lambda x: (1e1*(x[-1]-1)**4, (x[-2]-1.5)**4)
def dh(x):
r = zeros((2, p.n))
r[0,-1] = 1e1*4*(x[-1]-1)**3
r[1,-2] = 4*(x[-2]-1.5)**3 + 15 #incorrect derivative
return r
p.dh = dh
p.checkdf()
p.checkdc()
p.checkdh()
"""
you can use p.checkdF(x) for other point than x0 (F is f, c or h)
p.checkdc(myX)
or
p.checkdc(x=myX)
values with difference greater than
maxViolation (default 1e-5)
will be shown
p.checkdh(maxViolation=1e-4)
p.checkdh(myX, maxViolation=1e-4)
p.checkdh(x=myX, maxViolation=1e-4)
#print 'maxfun', p.maxfun
p.maxIter=50
# p.maxfun=100
#p.df_iter = 50
p.maxTime = 4000
h_args=(h,k,l,fq,fqerr,x,z,cosmat_list,coslist,flist)
if 0:
#p.h=[pos_sum,neg_sum]
p.h=[pos_sum,neg_sum]
p.c=[chisq]
# p.h=[pos_sum,neg_sum]
p.args.h=h_args
p.args.c=h_args
p.dh=[pos_sum_grad,neg_sum_grad]
p.df=chisq_grad
if 1:
#p.h=[pos_sum,neg_sum,chisq]
p.c=[chisq]
p.h=[pos_sum,neg_sum]
p.args.h=h_args
p.args.c=h_args
p.dh=[pos_sum_grad,neg_sum_grad]
p.dc=chisq_grad
#p.dh=[pos_sum_grad,neg_sum_grad,neg_sum_grad]
p.df = S_grad
if 0:
print 'checking'
#print 'maxfun', p.maxfun
p.maxIter = 50
# p.maxfun=100
#p.df_iter = 50
p.maxTime = 4000
h_args = (h, k, l, fq, fqerr, x, z, cosmat_list, coslist, flist)
if 0:
#p.h=[pos_sum,neg_sum]
p.h = [pos_sum, neg_sum]
p.c = [chisq]
# p.h=[pos_sum,neg_sum]
p.args.h = h_args
p.args.c = h_args
p.dh = [pos_sum_grad, neg_sum_grad]
p.df = chisq_grad
if 1:
#p.h=[pos_sum,neg_sum,chisq]
p.c = [chisq]
p.h = [pos_sum, neg_sum]
p.args.h = h_args
p.args.c = h_args
p.dh = [pos_sum_grad, neg_sum_grad]
p.dc = chisq_grad
#p.dh=[pos_sum_grad,neg_sum_grad,neg_sum_grad]
p.df = S_grad
if 0:
print 'checking'
r[2,35] = 2*x[35] + x[25]
return r
p.dc = DC
# non-linear equality constraints h(x) = 0
# 1e6*(x[last]-1)**4 = 0
# (x[last-1]-1.5)**4 = 0
p.h = lambda x: (1e4*(x[-1]-1)**4, (x[-2]-1.5)**4)
# dh(x)/dx: non-lin eq constraints gradients (optional):
def DH(x):
r = zeros((2, p.n))
r[0, -1] = 1e4*4 * (x[-1]-1)**3
r[1, -2] = 4 * (x[-2]-1.5)**3
return r
p.dh = DH
p.contol = 1e-3 # required constraints tolerance, default for NLP is 1e-6
# for ALGENCAN solver gtol is the only one stop criterium connected to openopt
# (except maxfun, maxiter)
# Note that in ALGENCAN gtol means norm of projected gradient of the Augmented Lagrangian
# so it should be something like 1e-3...1e-5
p.gtol = 1e-5 # gradient stop criterium (default for NLP is 1e-6)
# see also: help(NLP) -> maxTime, maxCPUTime, ftol and xtol
# that are connected to / used in lincher and some other solvers
# optional: check of user-supplied derivatives
p.checkdf()
def run_optimization(self, plot=True, _only_check_gradients=False):
"""Start/run optimization procedure and the optimum unless.
Set the keyword parameter 'plot' to False (default=True) if plotting
should not be conducted.
"""
grid = self.get_grid()
model = self.get_model()
# Initial try
p0 = self.get_p0()
# Less than (-0.5 < u < 1)
# TODO: These are currently hard coded. They shouldn't be.
#NLT = len(grid) * len(model.u)
#Alt = N.zeros( (NLT, len(p0)) )
#Alt[:, (len(grid) - 1) * len(model.x):] = -N.eye(len(grid) *
# len(model.u))
#blt = -0.5*N.ones(NLT)
# TODO: These are currently hard coded. They shouldn't be.
#N_xvars = (len(grid) - 1) * len(model.x)
#N_uvars = len(grid) * len(model.u)
#N_vars = N_xvars + N_uvars
#Alt = -N.eye(N_vars)
#blt = N.zeros(N_vars)
#blt[0:N_xvars] = -N.ones(N_xvars)*0.001
#blt[N_xvars:] = -N.ones(N_uvars)*1;
# Get OpenOPT handler
p = NLP(
self.f,
p0,
maxIter=1e3,
maxFunEvals=1e3,
#A=Alt, # See TODO above
#b=blt, # See TODO above
df=self.df,
ftol=1e-4,
xtol=1e-4,
contol=1e-4)
if len(grid) > 1:
p.h = self.h
p.dh = self.dh
if plot:
p.plot = 1
p.iprint = 1
if _only_check_gradients:
# Check gradients against finite difference quotients
p.checkdf(maxViolation=0.05)
p.checkdh()
return None
#opt = p.solve('ralg') # does not work - serious convergence issues
opt = p.solve('scipy_slsqp')
if plot:
plot_control_solutions(model, grid, opt.xf)
return opt
