def run(Args):
specs = read_specs(path_to_specs)
f, fjacx, fjacp = get_dynamics(specs)
if specs.get('generate_twin', False) is True:
generate_twin(specs, f)
optimizer = specs.get('optimizer', 'IPOPT')
print_level = 0 if charm else 5
if optimizer == 'IPOPT':
opt_options = {
'print_level': print_level,
'max_iter': max_iter_per_step, # Set the max number of iterations
# 'derivative_test' : 'first-order', # set derivative test
'tol': tol_per_step, # set termination criteria
'linear_solver': 'mumps',
'output_file': specs['data_folder'] + 'IPOPT.out'
}
if optimizer == 'SNOPT':
from snopt import SNOPT_options
opt_options = SNOPT_options()
opt_options.setOption('Print filename',
specs['data_folder'] + 'SNOPT.out')
opt_options.setOption('Iteration limit', max_iter_per_step)
opt_options.setOption('Optimality tolerance', tol_per_step)
if charm:
opt_options.setOption('Major print level', 0)
opt_options.setOption('Minor print level', 0)
opt_options.setOption('Verbose', False)
obs_dim = specs['obs_dim'] if specs['obs_dim'] != -1 else np.arange(
specs['num_dims'])
params = {
'name': specs['name'],
'data_folder': specs['data_folder'],
'data_file': specs['data_file'],
'stim_file': specs.get('stim_file'),
'nstart': specs.get('start_data', 0),
'ndata': specs.get('num_data', -1),
'f': f,
'fjacx': fjacx,
'fjacp': fjacp,
'alpha': specs['alpha'],
'max_beta': specs['max_beta'],
'Rm': specs['RM'],
'Rf0': specs['RF0'],
'Lidx': obs_dim,
'dt_model': specs['time_step'],
'optimizer': optimizer,
'opt_options': opt_options,
'num_pars': specs['num_par'],
'num_dims': specs['num_dims'],
'bounds': read_bounds(path_to_params),
}
rng = np.random.default_rng()
ss = rng.bit_generator._seed_seq
init_seeds = ss.spawn(num_init_cond)
if charm:
sol = np.array(charm.pool.map(partial(min_action, params=params),
init_seeds),
dtype=object)
np.savez(specs['data_folder'] + specs['name'] + '_results.npz',
path=np.array(sol[:, 0]),
params=np.array(sol[:, 1]),
action=np.array(sol[:, 2]),
time=np.array(sol[0, 3]),
**specs)
else:
sol = min_action(init_seeds[0], params)
np.savez(specs['data_folder'] + specs['name'] + '_results.npz',
path=sol[0],
params=sol[1],
action=sol[2],
time=sol[3],
**specs)
exit()
# Nonlinear objective term only
sumx = x[0] + x[1] + x[2]
fObj = 0.0
fObj = sumx**2
#gObj[0] = 2.0*sum
#gObj[1] = 2.0*sum
#gObj[2] = 2.0*sum
return mode, fObj, gObj, fCon, gCon
options = SNOPT_options()
inf = 1.0e+20
options.setOption('Infinite bound', inf)
options.setOption('Verify level', 3)
options.setOption('Print filename', 'sntoyc.out')
m = 4
n = 4
nnCon = 2
nnJac = 2
nnObj = 3
x0 = np.zeros(n + m, float)
# J contains the sparsity pattern of the Jacobian matrix.
# For nonlinear elements, enter any nonzero number (in this case 100).
# Linear elements must be correctly defined.
import numpy as np
import scipy.sparse as sp
from snopt import snopta, SNOPT_options
def dieta_fun(status,x,F,G,needF,needG):
# LP has no nonlinear terms in the objective
F = []
G = []
return status, F, G
inf = 1.0e20
options = SNOPT_options()
options.setOption('Print filename','dieta.out')
options.setOption('Minor print level',1)
options.setOption('Summary frequency',1)
options.setOption('Print frequency',1)
n = 6
nF = 4
ObjRow = 4
# We provide the linear components of the Jacobian
# matrix as a dense matrix.
A = np.array([ [110, 205, 160, 160, 420, 260],
[ 4, 32, 13, 8, 4, 14],
[ 2, 12, 54, 285, 22, 80],
[ 3, 24, 13, 9, 20, 19]],float)
gObj[1] = -x[5]
gObj[2] = -x[6] + x[7]
gObj[3] = x[8]
gObj[4] = -x[7]
gObj[5] = -x[1]
gObj[6] = -x[2] + x[0]
gObj[7] = -x[4] + x[2]
gObj[8] = x[3]
return mode, fObj, gObj
inf = 1.0e+20
options = SNOPT_options()
options.setOption('Infinite bound', inf)
options.setOption('Specs filename', 'snmainb.spc')
options.setOption('Print filename', 'snmainb.out')
m = 18
n = 9
nnCon = 14
ne = 52
nnJac = n
nnObj = n
bl = -inf * np.ones(n + m)
bu = inf * np.ones(n + m)
# Nonlinear constraints
bu[1 + n:nnCon + n] = 1.0
fObj = 100.0 * (x[1] - x[0]**2)**2 + (1.0 - x[0])**2
if mode == 0:
return mode, fObj
if mode == 1 or mode == 2:
gObj[0] = -400.0 * (x[1] - x[0]**2) * x[0] - 2.0 * (1.0 - x[0])
gObj[1] = 200.0 * (x[1] - x[0]**2)
return mode, fObj, gObj
options = SNOPT_options()
inf = 1.0e+20
options.setOption('Infinite bound', inf)
options.setOption('Verify level', 3)
options.setOption('Verbose', True)
options.setOption('Print filename', 't2banana.out')
m = 1
n = 2
nnCon = 0
nnJac = 0
nnObj = n
x0 = np.zeros(n + m, float)
x0[0] = 1.2
x0[1] = 1.0
al = np.array([
-7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0,
60.0, 50.0, 70.0, 85.0, 100.0
])
au = np.array([
6.0, 6.0, 6.0, 6.0, 7.0, 7.0, 7.0, 7.0, 6.0, 6.0, 6.0, 6.0, inf, inf, inf,
inf, inf
])
c = np.array([
2.3, 1.7, 2.2, 2.3, 1.7, 2.2, 2.3, 1.7, 2.2, 2.3, 1.7, 2.2, 2.3, 1.7, 2.2
])
# Solve the problem
options = SNOPT_options()
options.setOption('Print frequency', 1)
options.setOption('Summary frequency', 1)
result = sqopt(userHx,
x0,
xl=xl,
xu=xu,
A=A,
al=al,
au=au,
c=c,
name='HS118 ',
options=options)
F[0] = x[1] # objective row
F[1] = x[0]**2 + 4.0 * x[1]**2
F[2] = (x[0] - 2.0)**2 + x[1]**2
G[0] = 2 * x[0]
G[1] = 8 * x[1]
G[2] = 2 * (x[0] - 2)
G[3] = 2 * x[1]
return status, F, G
inf = 1.0e20
options = SNOPT_options()
options.setOption('Verbose', True)
options.setOption('Solution print', True)
options.setOption('Print filename', 'sntoya.out')
options.setOption('Summary frequency', 1)
# Name arrays have to be dtype='|S1' and also have to be the
# correct length, else they are ignored by SNOPT:
xnames = np.empty(2, dtype='|S8')
xnames[0] = " x0"
xnames[1] = " x1"
Fnames = np.empty(3, dtype='|S8')
Fnames[0] = " F0"
Fnames[1] = " F1"
Fnames[2] = " F2"