def IPOPTrunINIT(vars0,mission,includeDrag):
import ipopt
import constraints as ct
import costFunction as CF
nvar = len(vars0)
con1= ct.eval_g(vars0,mission)
g_L,g_U = ct.bounds(vars0,mission)
ncon = len(g_L)
jac = ct.eval_jac_g(vars0,mission,'1d')
#print np.shape(jac),ncon,len(vars0)
check = CF.fprimeObjectiveMassINIT(vars0,mission)
x_L,x_U = ct.getXLXU(vars0,mission)
# start IPOPT classess
class trajectory(object):
def __init__(self,mission,includeDrag):
self._mission = mission
self._includeDrag = includeDrag
def objective(self,x):
ret = CF.ObjectiveMassINIT(x,self._mission)
return ret
def gradient(self,x):
ret = CF.fprimeObjectiveMassINIT(x,self._mission)
return ret
def constraints(self,x):
ret = ct.eval_g(x,self._mission,self._includeDrag)
return ret
def jacobian(self,x):
ret = ct.eval_jac_g(x,self._mission,'1d',self._includeDrag)
return ret
nlp = ipopt.problem(
n=len(vars0),
m=ncon,
problem_obj=trajectory(mission,includeDrag),
lb=x_L,
ub=x_U,
cl=g_L,
cu=g_U
)
#
# Set solver options
#
nlp.addOption('mu_strategy', 'adaptive')
nlp.addOption('tol', 5e-6)
nlp.addOption('max_iter',1000)
nlp.addOption('acceptable_constr_viol_tol',5e-6)
nlp.addOption('constr_viol_tol',5e-6)
#nlp.addOption('derivative_test','first-order')
nlp.addOption('acceptable_tol',5e-6)
nlp.addOption('expect_infeasible_problem','yes')
x,info = nlp.solve(vars0)
return x,info
def optimizeSFA(self, print_level=0, max_iter=50):
# create problem
if self.constraint_matrix is None:
handle = ipopt.problem(n=self.k,
m=0,
problem_obj=sfaObj(self),
lb=self.uprior[0],
ub=self.uprior[1])
else:
handle = ipopt.problem(n=self.k,
m=self.constraint_matrix.shape[0],
problem_obj=sfaObj(self),
lb=self.uprior[0],
ub=self.uprior[1],
cl=self.constraint_values[0],
cu=self.constraint_values[1])
# add options
handle.addOption('print_level', print_level)
if max_iter is not None: handle.addOption('max_iter', max_iter)
# initial point
if self.soln is None:
beta0 = np.linalg.solve(self.X.T.dot(self.X), self.X.T.dot(self.Y))
gama0 = np.repeat(0.01, self.k_gama)
deta0 = np.repeat(0.01, self.k_deta)
x0 = np.hstack((beta0, gama0, deta0))
else:
x0 = self.soln
# solver the problem
soln, info = handle.solve(x0)
# extract the solution
self.soln = soln
self.info = info
self.beta_soln = soln[self.id_beta]
self.gama_soln = soln[self.id_gama]
self.deta_soln = soln[self.id_deta]
def solve(self, x0=None, tee=False):
if not self._is_composite:
cyipopt_solver = ipopt.problem(n=self._nlp.nx,
m=self._nlp.ng,
problem_obj=self._problem,
lb=self._nlp.xl(),
ub=self._nlp.xu(),
cl=self._nlp.gl(),
cu=self._nlp.gu())
else:
xl = self._nlp.xl()
xu = self._nlp.xu()
gl = self._nlp.gl()
gu = self._nlp.gu()
nx = int(self._nlp.nx)
ng = int(self._nlp.ng)
cyipopt_solver = ipopt.problem(n=nx,
m=ng,
problem_obj=self._problem,
lb=xl.flatten(),
ub=xu.flatten(),
cl=gl.flatten(),
cu=gu.flatten())
if x0 is None:
xstart = self._nlp.x_init()
if self._is_composite:
xstart = xstart.flatten()
else:
assert isinstance(x0, np.ndarray)
assert x0.size == self._nlp.nx
xstart = x0
# this is needed until NLP hessian takes obj_factor as an input
if not self._nlp._future_libraries:
cyipopt_solver.addOption('nlp_scaling_method', 'none')
# add options
for k, v in self._options.items():
cyipopt_solver.addOption(k, v)
if tee:
x, info = cyipopt_solver.solve(xstart)
else:
newstdout = redirect_stdout()
x, info = cyipopt_solver.solve(xstart)
os.dup2(newstdout, 1)
return x, info
def solve(self, x0=None, tee=False):
xl = self._problem.x_lb()
xu = self._problem.x_ub()
gl = self._problem.g_lb()
gu = self._problem.g_ub()
if x0 is None:
x0 = self._problem.x_init()
xstart = x0
nx = len(xstart)
ng = len(gl)
cyipopt_solver = ipopt.problem(n=nx,
m=ng,
problem_obj=self._problem,
lb=xl,
ub=xu,
cl=gl,
cu=gu
)
# add options
for k, v in self._options.items():
cyipopt_solver.addOption(k, v)
if tee:
x, info = cyipopt_solver.solve(xstart)
else:
newstdout = redirect_stdout()
x, info = cyipopt_solver.solve(xstart)
os.dup2(newstdout, 1)
return x, info
def solve(self, prob):
cur_conds = prob.conditions
comps = prob.pure_elements
nlp = ipopt.problem(
n=prob.num_vars,
m=prob.num_constraints,
problem_obj=prob,
lb=prob.xl,
ub=prob.xu,
cl=prob.cl,
cu=prob.cu
)
length_scale = np.min(np.abs(prob.cl))
length_scale = max(length_scale, 1e-9)
nlp.addOption(b'print_level', 0)
if not self.verbose:
# suppress the "This program contains Ipopt" banner
nlp.addOption(b'sb', b'yes')
nlp.addOption(b'tol', 1e-1)
nlp.addOption(b'constr_viol_tol', 1e-12)
# This option improves convergence when using L-BFGS
nlp.addOption(b'limited_memory_max_history', 100)
nlp.addOption(b'max_iter', 200)
x, info = nlp.solve(prob.x0)
dual_inf = np.max(np.abs(info['mult_g']*info['g']))
if dual_inf > MAX_SOLVE_DRIVING_FORCE:
if self.verbose:
print('Trying to improve poor solution')
# Constraints are getting tiny; need to be strict about bounds
if length_scale < 1e-6:
nlp.addOption(b'compl_inf_tol', 1e-15)
nlp.addOption(b'bound_relax_factor', 1e-12)
# This option ensures any bounds failures will fail "loudly"
# Otherwise we are liable to have subtle mass balance errors
nlp.addOption(b'honor_original_bounds', b'no')
else:
nlp.addOption(b'dual_inf_tol', MAX_SOLVE_DRIVING_FORCE)
accurate_x, accurate_info = nlp.solve(x)
if accurate_info['status'] >= 0:
x, info = accurate_x, accurate_info
chemical_potentials = -np.array(info['mult_g'])[-len(set(comps) - {'VA'}):]
if info['status'] == -10:
# Not enough degrees of freedom; nothing to do
if len(prob.composition_sets) == 1:
converged = True
chemical_potentials[:] = prob.composition_sets[0].energy
else:
converged = False
elif info['status'] < 0:
if self.verbose:
print('Calculation Failed: ', cur_conds, info['status_msg'])
converged = False
else:
converged = True
if self.verbose:
print('Chemical Potentials', chemical_potentials)
print(info['mult_x_L'])
print(x)
print('Status:', info['status'], info['status_msg'])
return SolverResult(converged=converged, x=x, chemical_potentials=chemical_potentials)
def optimize(self, x0=None, print_level=0, max_iter=100, tol=1e-8):
if x0 is None:
x0 = np.hstack((self.beta, self.gamma, self.delta))
if self.use_lprior:
x0 = np.hstack((x0, np.zeros(self.k)))
assert x0.size == self.k_total
opt_problem = ipopt.problem(n=self.k_total,
m=self.num_constraints,
problem_obj=self,
lb=self.uprior[0],
ub=self.uprior[1],
cl=self.cl,
cu=self.cu)
opt_problem.addOption('print_level', print_level)
opt_problem.addOption('max_iter', max_iter)
opt_problem.addOption('tol', tol)
# opt_problem.addOption('bound_push', 1e-15)
# opt_problem.addOption('bound_frac', 1e-15)
soln, info = opt_problem.solve(x0)
self.soln = soln
self.info = info
self.beta = soln[self.idx_beta]
self.gamma = soln[self.idx_gamma]
self.delta = soln[self.idx_delta]
def solve_direct(self):
#start values
x0 = np.concatenate([
np.ones((self.par['n_b'])) * self.bar_diam,
np.zeros((self.par['n_dl']))
])
#parameter bounds - bar diameters and all dislocations
lb = self.bounds()[0]
ub = self.bounds()[1]
#constraints
cl = self.limits()[0]
cu = self.limits()[1]
#define problem for ipopt
problem_ipopt = ipopt.problem(n=self.par['n_var'],
m=len(cl),
problem_obj=self,
lb=lb,
ub=ub,
cl=cl,
cu=cu)
#add options for ipopt
add_option_ipopt(self, problem=problem_ipopt)
#solve
out_opt, out_info = problem_ipopt.solve(x0)
return out_opt, out_info
def IPOPTrunINIT(vars0, mission, includeDrag):
import ipopt
import constraints as ct
import costFunction as CF
nvar = len(vars0)
con1 = ct.eval_g(vars0, mission)
g_L, g_U = ct.bounds(vars0, mission)
ncon = len(g_L)
jac = ct.eval_jac_g(vars0, mission, '1d')
#print np.shape(jac),ncon,len(vars0)
check = CF.fprimeObjectiveMassINIT(vars0, mission)
x_L, x_U = ct.getXLXU(vars0, mission)
# start IPOPT classess
class trajectory(object):
def __init__(self, mission, includeDrag):
self._mission = mission
self._includeDrag = includeDrag
def objective(self, x):
ret = CF.ObjectiveMassINIT(x, self._mission)
return ret
def gradient(self, x):
ret = CF.fprimeObjectiveMassINIT(x, self._mission)
return ret
def constraints(self, x):
ret = ct.eval_g(x, self._mission, self._includeDrag)
return ret
def jacobian(self, x):
ret = ct.eval_jac_g(x, self._mission, '1d', self._includeDrag)
return ret
nlp = ipopt.problem(n=len(vars0),
m=ncon,
problem_obj=trajectory(mission, includeDrag),
lb=x_L,
ub=x_U,
cl=g_L,
cu=g_U)
#
# Set solver options
#
nlp.addOption('mu_strategy', 'adaptive')
nlp.addOption('tol', 5e-6)
nlp.addOption('max_iter', 1000)
nlp.addOption('acceptable_constr_viol_tol', 5e-6)
nlp.addOption('constr_viol_tol', 5e-6)
#nlp.addOption('derivative_test','first-order')
nlp.addOption('acceptable_tol', 5e-6)
nlp.addOption('expect_infeasible_problem', 'yes')
x, info = nlp.solve(vars0)
return x, info
def optimize(self,
parameters,
loss,
max_iters=1,
verbose=False,
*args,
**kwargs):
"""
Optimize the given loss function with respect to the given parameters.
Args:
parameters (np.array): parameters to optimize.
loss (callable): callable objective / loss function to minimize.
bounds (tuple, list, np.array): parameter bounds. E.g. bounds=[0, np.inf]
max_iters (int): number of maximum iterations.
verbose (bool): if True, it will display information during the optimization process.
*args: list of arguments to give to the loss function if callable.
**kwargs: dictionary of arguments to give to the loss function if callable.
Returns:
float, torch.Tensor, np.array: loss scalar value.
object: best parameters
"""
N = len(parameters)
# define initial value
x0 = parameters # important that the initial value != 0 for the computation of the grad!
# define (lower and upper) bound constraints
lb = [-1] * N
ub = [1] * N
# define constraints; if upper and lower constraints (resp. cu and cl) are equal then equality constraint
cl = [1] + [0] * (N - 1)
cu = [1] + [0] * (N - 1)
# create ipopt (which contains the objective function, its gradients, and constraints)
opt = _IPopt(verbose=False)
opt.add_constraint(NormConstraint())
opt.add_constraint(OrthogonalConstraint(x))
# define the nonlinear optimization problem
self.optimizer = ipopt.problem(n=N,
m=len(cl[:i]),
problem_obj=opt,
lb=lb,
ub=ub,
cl=cl[:i],
cu=cu[:i])
# solve problem
x, info = self.optimizer.solve(x0)
# save the results
self.best_parameters = x
self.best_result = info['obj_val']
return self.best_result, self.best_parameters
def solve(self, prob):
cur_conds = prob.conditions
comps = prob.components
nlp = ipopt.problem(n=prob.num_vars,
m=prob.num_constraints,
problem_obj=prob,
lb=prob.xl,
ub=prob.xu,
cl=prob.cl,
cu=prob.cu)
length_scale = np.min(np.abs(prob.cl))
length_scale = max(length_scale, 1e-9)
nlp.addOption(b'print_level', 0)
nlp.addOption(b'tol', 1e-1)
nlp.addOption(b'constr_viol_tol', 1e-12)
# This option improves convergence when using L-BFGS
nlp.addOption(b'limited_memory_max_history', 100)
nlp.addOption(b'max_iter', 200)
x, info = nlp.solve(prob.x0)
dual_inf = np.max(np.abs(info['mult_g'] * info['g']))
if dual_inf > MAX_SOLVE_DRIVING_FORCE:
if self.verbose:
print('Trying to improve poor solution')
# Constraints are getting tiny; need to be strict about bounds
if length_scale < 1e-6:
nlp.addOption(b'compl_inf_tol', 1e-15)
nlp.addOption(b'bound_relax_factor', 1e-12)
# This option ensures any bounds failures will fail "loudly"
# Otherwise we are liable to have subtle mass balance errors
nlp.addOption(b'honor_original_bounds', b'no')
else:
nlp.addOption(b'dual_inf_tol', MAX_SOLVE_DRIVING_FORCE)
accurate_x, accurate_info = nlp.solve(x)
if accurate_info['status'] >= 0:
x, info = accurate_x, accurate_info
chemical_potentials = -np.array(
info['mult_g'])[-len(set(comps) - {'VA'}):]
if info['status'] == -10:
# Not enough degrees of freedom; nothing to do
if len(prob.composition_sets) == 1:
converged = True
chemical_potentials[:] = prob.composition_sets[0].energy
else:
converged = False
elif info['status'] < 0:
if self.verbose:
print('Calculation Failed: ', cur_conds, info['status_msg'])
converged = False
else:
converged = True
if self.verbose:
print('Chemical Potentials', chemical_potentials)
print(info['mult_x_L'])
print(x)
print('Status:', info['status'], info['status_msg'])
return SolverResult(converged=converged,
x=x,
chemical_potentials=chemical_potentials)
def main():
#
# Define the problem
#
x0 = [1.0, 5.0, 5.0, 1.0]
lb = [1.0, 1.0, 1.0, 1.0]
ub = [5.0, 5.0, 5.0, 5.0]
cl = [25.0, 40.0]
cu = [2.0e19, 40.0]
nlp = ipopt.problem(
n=len(x0),
m=len(cl),
problem_obj=hs071(),
lb=lb,
ub=ub,
cl=cl,
cu=cu
)
#
# Set solver options
#
#nlp.addOption('derivative_test', 'second-order')
nlp.addOption('mu_strategy', 'adaptive')
nlp.addOption('tol', 1e-7)
#
# Scale the problem (Just for demonstration purposes)
#
nlp.setProblemScaling(
obj_scaling=2,
x_scaling=[1, 1, 1, 1]
)
nlp.addOption('nlp_scaling_method', 'user-scaling')
nlp.addOption('linear_solver', 'mumps')
# nlp.addOption('linear_solver', 'ma57')
# nlp.addOption('print_level', 5)
nlp.addOption('file_print_level', 5)
nlp.addOption('output_file', 'py_ipopt2.out')
#
# Solve the problem
#
x, info = nlp.solve(x0)
print("Solution of the primal variables: x=%s\n" % repr(x))
print("Solution of the dual variables: lambda=%s\n" % repr(info['mult_g']))
print("Objective=%s\n" % repr(info['obj_val']))
print("All done")
def __init__(self, params):
beta = params['beta']
k0 = params['k0']
f0 = params['f0']
rmin = params['rmin']
max_iter = params['max_iter']
self.tag = params['tag']
self.debug = params['debug']
self.cantilever_key = params['cantilever']
self.dir = params['dir']
to_connect = params['to_connect']
obj_scale = params['obj_scale']
pmu = params['pmu']
self.material = materials.PiezoMumpsMaterial()
self.cantilever = self.select_cantilever()
self.la = LaminateAnalysis(self.cantilever, self.material, to_connect,
pmu)
self.analyser = analysers.CantileverAnalyser(self.la.fem)
self.sym = symmetry.Symmetry(self.la.fem)
self.density_filter = density_filter.DensityFilter(self.la.fem, rmin)
self.projection = projection.Projection(beta)
self.x0 = self.sym.initial(self.la.fem.mesh.get_densities())
# Attributes not set in contructor.
self.xs_prev = None
self.neta1 = 0.0
self.k1 = 0.0
self.f1 = 0.0
self.solution = None
self.info = None
self.dneta_dp = None
self.df1_dp = None
self.dk1_dp = None
self.records = []
# Initialise the nonlinear optimizer.
inf = 10e19
self.nlp = ipopt.problem(n=self.sym.dimension,
m=2,
problem_obj=self,
lb=1e-4 * np.ones(self.sym.dimension),
ub=np.ones(self.sym.dimension),
cl=np.array((-inf, f0)),
cu=np.array((k0, inf)))
# Configure the nonlinear optimizer.
log_file = ''.join((self.dir, '/', self.tag, '-log.txt')).encode()
self.nlp.addOption(b'max_iter', max_iter)
self.nlp.addOption(b'tol', 1e-5)
self.nlp.addOption(b'acceptable_tol', 1e-3)
self.nlp.addOption(b'obj_scaling_factor', obj_scale)
self.nlp.addOption(b'output_file', log_file)
self.nlp.addOption(b'expect_infeasible_problem', b'yes')
def fit(self,
x_init: np.ndarray,
data: Optional[Data] = None,
options: Optional[Dict[str, Any]] = None):
problem_obj = _IPOPTProblem(self.model, data)
if has_bounds(self.model) and has_constraints(self.model):
problem = ipopt.problem(
n=len(x_init),
m=len(self.model.C),
problem_obj=problem_obj,
lb=self.model.lb,
ub=self.model.ub,
cl=self.model.c_lb,
cu=self.model.c_ub,
)
elif has_bounds(self.model):
problem_obj.constraints = None
problem_obj.jacobian = None
problem = ipopt.problem(
n=len(x_init),
m=0,
problem_obj=problem_obj,
lb=self.model.lb,
ub=self.model.ub,
)
elif has_constraints(self.model):
problem = ipopt.problem(
n=len(x_init),
m=len(self.model.C),
problem_obj=problem_obj,
cl=self.model.c_lb,
cu=self.model.c_ub,
)
else:
problem_obj.constraints = None
problem_obj.jacobian = None
problem = ipopt.problem(n=len(x_init),
m=0,
problem_obj=problem_obj)
for name, val in options['solver_options'].items():
problem.addOption(name, options['solver_options'][val])
self.x_opt, self.info = problem.solve(x_init)
self.fun_val_opt = problem_obj.objective(self.x_opt)
def convex_formulation_hard_constraints():
''' Definition the bilinear problem '''
x0 = [2, 4, 40.0, 10.0]
lb = [1.0, 0.0, -1000.0, -1000.0]
ub = [3.0, 20.0, 1000.0, 1000.0]
cl = [6.0, 0.0, 0.0, 0.0, 0.0]
cu = [6.0, +2e19, +2e19, +2e19, +2e19]
nlpConvex = ipopt.problem(
n=len(x0),
m=len(cl),
problem_obj=buildConvexFormulationHardConstraint(x0),
lb=lb,
ub=ub,
cl=cl,
cu=cu
)
#
# Set solver options
#
#nlp.addOption('derivative_test', 'second-order')
nlpConvex.addOption('mu_strategy', 'adaptive')
nlpConvex.addOption('jacobian_approximation', 'finite-difference-values')
nlpConvex.addOption('hessian_approximation', 'limited-memory')
nlpConvex.addOption('tol', 1e-6)
#nlpConvex.addOption('acceptable_tol', 1e-9)
nlpConvex.addOption('dual_inf_tol', 1e-6)
nlpConvex.addOption('constr_viol_tol', 1e-6)
nlpConvex.addOption('compl_inf_tol', 1e-6)
#
# Scale the problem (Just for demonstration purposes)
#
nlpConvex.setProblemScaling(
obj_scaling=1,
x_scaling=[1.0,1.0,1.0,1.0]
)
nlpConvex.addOption('nlp_scaling_method', 'user-scaling')
#
# Solve the problem
#
x, info = nlpConvex.solve(x0)
print "Solution of the primal variables: x=%s\n" % repr(x)
print "Solution of the dual variables: lambda=%s\n" % repr(info['mult_g'])
print "Objective=%s\n" % repr(info['obj_val'])
print colored('torque check: ', 'red'), x[0]*x[1]
def main():
#
# Define the problem
#
x0 = [1.0, 5.0, 5.0, 1.0]
lb = [1.0, 1.0, 1.0, 1.0]
ub = [5.0, 5.0, 5.0, 5.0]
cl = [25.0, 40.0]
cu = [2.0e19, 40.0]
nlp = ipopt.problem(
n=len(x0),
m=len(cl),
problem_obj=hs071(),
lb=lb,
ub=ub,
cl=cl,
cu=cu
)
#
# Set solver options
#
#nlp.addOption('derivative_test', 'second-order')
nlp.addOption('mu_strategy', 'adaptive')
nlp.addOption('tol', 1e-7)
#
# Scale the problem (Just for demonstration purposes)
#
nlp.setProblemScaling(
obj_scaling=2,
x_scaling=[1, 1, 1, 1]
)
nlp.addOption('nlp_scaling_method', 'user-scaling')
#
# Solve the problem
#
x, info = nlp.solve(x0)
print "Solution of the primal variables: x=%s\n" % repr(x)
print "Solution of the dual variables: lambda=%s\n" % repr(info['mult_g'])
print "Objective=%s\n" % repr(info['obj_val'])
def solve(self, x0=None, tee=False):
xl = self._problem.x_lb()
xu = self._problem.x_ub()
gl = self._problem.g_lb()
gu = self._problem.g_ub()
if x0 is None:
x0 = self._problem.x_init()
xstart = x0
nx = len(xstart)
ng = len(gl)
cyipopt_solver = ipopt.problem(n=nx,
m=ng,
problem_obj=self._problem,
lb=xl,
ub=xu,
cl=gl,
cu=gu)
# check if we need scaling
obj_scaling, x_scaling, g_scaling = self._problem.scaling_factors()
if obj_scaling is not None or x_scaling is not None or g_scaling is not None:
# need to set scaling factors
if obj_scaling is None:
obj_scaling = 1.0
if x_scaling is None:
x_scaling = np.ones(nx)
if g_scaling is None:
g_scaling = np.ones(ng)
cyipopt_solver.setProblemScaling(obj_scaling, x_scaling, g_scaling)
# add options
for k, v in self._options.items():
cyipopt_solver.addOption(k, v)
if tee:
x, info = cyipopt_solver.solve(xstart)
else:
newstdout = redirect_stdout()
x, info = cyipopt_solver.solve(xstart)
os.dup2(newstdout, 1)
return x, info
def main():
#
# Define the problem
#
x0 = [1.0, 5.0, 5.0, 1.0]
lb = [1.0, 1.0, 1.0, 1.0]
ub = [5.0, 5.0, 5.0, 5.0]
cl = [25.0, 40.0]
cu = [2.0e19, 40.0]
nlp = ipopt.problem(n=len(x0),
m=len(cl),
problem_obj=hs071(),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
#
# Set solver options
#
#nlp.addOption('derivative_test', 'second-order')
nlp.addOption('mu_strategy', 'adaptive')
nlp.addOption('jacobian_approximation', 'finite-difference-values')
nlp.addOption('hessian_approximation', 'limited-memory')
nlp.addOption('tol', 1e-7)
#
# Scale the problem (Just for demonstration purposes)
#
nlp.setProblemScaling(obj_scaling=2, x_scaling=[1, 1, 1, 1])
nlp.addOption('nlp_scaling_method', 'user-scaling')
#
# Solve the problem
#
x, info = nlp.solve(x0)
print "Solution of the primal variables: x=%s\n" % repr(x)
print "Solution of the dual variables: lambda=%s\n" % repr(info['mult_g'])
print "Objective=%s\n" % repr(info['obj_val'])
def initialize(self, beta0=None, print_level=0, max_iter=20):
if beta0 is None:
beta0 = np.repeat(0.1, self.k)
assert beta0.size == self.k
opt_problem = ipopt.problem(n=self.k,
m=2 * self.N,
problem_obj=self,
cl=self.c[0],
cu=self.c[1])
opt_problem.addOption('print_level', print_level)
opt_problem.addOption('max_iter', max_iter)
beta_soln, info = opt_problem.solve(beta0)
self.beta_soln = beta_soln
self.info = info
return beta_soln
def optimize(self,
x0=None,
print_level=0,
max_iter=100,
tol=1e-8,
acceptable_tol=1e-6,
nlp_scaling_method=None,
nlp_scaling_min_value=None):
if x0 is None:
x0 = np.hstack((self.beta, self.gamma, self.delta))
if self.use_lprior:
x0 = np.hstack((x0, np.zeros(self.k)))
assert x0.size == self.k_total
opt_problem = ipopt.problem(n=int(self.k_total),
m=int(self.num_constraints),
problem_obj=self,
lb=self.uprior[0],
ub=self.uprior[1],
cl=self.cl,
cu=self.cu)
opt_problem.addOption('print_level', print_level)
opt_problem.addOption('max_iter', max_iter)
opt_problem.addOption('tol', tol)
opt_problem.addOption('acceptable_tol', acceptable_tol)
if nlp_scaling_method is not None:
opt_problem.addOption('nlp_scaling_method', nlp_scaling_method)
if nlp_scaling_min_value is not None:
opt_problem.addOption('nlp_scaling_min_value',
nlp_scaling_min_value)
soln, info = opt_problem.solve(x0)
self.soln = soln
self.info = info
self.beta = soln[self.idx_beta]
self.gamma = soln[self.idx_gamma]
self.delta = soln[self.idx_delta]
def solve(self):
nlp = ipopt.problem(n=self.n_,
m=self.m_,
problem_obj=self,
lb=self.lowerBounds,
ub=self.upperBounds,
cl=self.constraintsLowerBounds,
cu=self.constraintsUpperBounds)
nlp.addOption(b'print_level', 5)
nlp.addOption(b'file_print_level', 12)
nlp.addOption(b'output_file', b'ipopt_logs.txt')
# nlp.addOption(b'derivative_test_tol',5.0e-5)
# nlp.addOption(b'derivative_test', b'first-order')
# nlp.addOption(b'derivative_test_perturbation', 1.0e-6)
nlp.addOption(b'tol', 5.0e-6)
nlp.addOption(b'max_iter', 500)
nlp.addOption(b'hessian_approximation', b'limited-memory')
nlp.addOption(b'jac_c_constant', b'yes')
x0 = self.cost_.get_first_guess()
return nlp.solve(x0)
def subproblem_ALM(self):
#parameter bounds - bar diameters and all dislocations
lb = self.bounds()[0]
ub = self.bounds()[1]
#constraints
cl = self.limits()[0]
cu = self.limits()[1]
#shortcut ipopt
self.par_ALM['m'] = len(cl)
#define problem for ipopt
problem_ipopt = ipopt.problem(n=self.par_ALM['n'],
m=self.par_ALM['m'],
problem_obj=self,
lb=lb,
ub=ub,
cl=cl,
cu=cu)
add_option_ipopt(self, problem=problem_ipopt)
opt, info = problem_ipopt.solve(self.par_ALM['x'])
self.par_ALM['x'] = opt
self.par_ALM['mult_sub'] = info['mult_g']
self.par_ALM['mult_lb'] = info['mult_x_L']
self.par_ALM['mult_ub'] = info['mult_x_U']
if self.verbose:
print(info['status_msg'])
print('x\t', self.par_ALM['x'])
return
return x[0]
def jacobian(self, x):
return [1.0, 0.0]
lb = None # np.ones([N,1])*(-3)
ub = None # np.ones([N,1])*(3)
cl = [1.5]
cu = None
p = ipopt.problem(
n=2,
m=1,
problem_obj=nlp(),
lb=lb,
ub=ub,
cl=cl,
cu=cu
)
p.addOption('print_level', 0)
p.addOption('max_iter', 20)
x0 = [0,0]
xstar, info = p.solve(x0)
print(xstar)
cy_theta = np.linspace(0.5 * pi, -1.5 * pi, Nnode)
cy_thetad = np.zeros_like(cy_theta) + (cy_theta[1] - cy_theta[0]) / interval
model = VirtualCyclingSimulator(cy_theta, Nnode, nDoF, interval, lamda,
Human_const, Exo_const, Bike_param)
# the joint angles reference from inverse kinematics
joints_ref = generate_joints(model, cy_theta, Bike_param, Exo_const, Nnode,
interval)
nlp = ipopt.problem(n=Nnode * nDoF + (Nnode - 1) * int(nDoF / 2 - 1),
m=nDoF * (Nnode - 1),
problem_obj=VirtualCyclingSimulator(
cy_theta, Nnode, nDoF, interval, lamda, Human_const,
Exo_const, Bike_param),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
nlp.addOption(b'linear_solver', b'MA86')
nlp.addOption(b'max_iter', 10000)
nlp.addOption(b'hessian_approximation', b'limited-memory')
nlp.addOption(b'tol', 1e-4)
nlp.addOption(b'acceptable_tol', 1e-3)
nlp.addOption(b'max_cpu_time', 1e+5)
x_init = np.zeros(Nnode * nDoF + (Nnode - 1) * int(nDoF / 2 - 1))
x_init[lhip] = joints_ref[:, 0] + 0.001 * np.random.random(Nnode)
def setup(self, u0):
N = self.N
T = self.T
t0 = self.t0
x0 = self.x0
nu = self.nu
lanes = self.lanes
obstacle = self.obstacle
posIdx = self.posIdx
ns_option = self.ns_option
if ns == 6:
lb_Vddot = np.ones([N, 1]) * lb_VddotVal
lb_Chiddot = np.ones([N, 1]) * lb_ChiddotVal
ub_Vddot = np.ones([N, 1]) * ub_VddotVal
ub_Chiddot = np.ones([N, 1]) * ub_ChiddotVal
lb = np.concatenate([lb_Vddot, lb_Chiddot])
ub = np.concatenate([ub_Vddot, ub_Chiddot])
elif ns == 4:
lb_Vdot = np.ones([N, 1]) * lb_VdotVal
lb_Chidot = np.ones([N, 1]) * lb_ChidotVal
ub_Vdot = np.ones([N, 1]) * ub_VdotVal
ub_Chidot = np.ones([N, 1]) * ub_ChidotVal
lb = np.concatenate([lb_Vdot, lb_Chidot])
ub = np.concatenate([ub_Vdot, ub_Chidot])
lataccel_max = lataccel_maxVal
# Running Constraints
# u = u0.flatten(1)
# x = prob.computeOpenloopSolution(u, N, T, t0, x0)
if obstacle.Present == True:
lane1Lines = lanes.lane1Lines
lane2Lines = lanes.lane2Lines
acrossLines = lanes.acrossLines
idx_Vehicle, laneNo = lanes.insideRoadSegment(
x0[0], x0[1], lane1Lines, lane2Lines, acrossLines)
if (idx_Vehicle < obstacle.idx_StartSafeZone):
dyRoadL = delta_yRoad
dyRoadR = delta_yRoad
elif (idx_Vehicle >= obstacle.idx_StartSafeZone) and (
idx_Vehicle < obstacle.idx_EndSafeZone):
dyRoadL = delta_yRoad
dyRoadR = delta_yRoadRelaxed
elif (idx_Vehicle >= obstacle.idx_StartObstacle) and (
idx_Vehicle < obstacle.idx_EndObstacle):
dyRoadL = delta_yRoad
dyRoadR = delta_yRoad
else:
dyRoadL = delta_yRoad
dyRoadR = delta_yRoad
else:
dyRoadL = delta_yRoad
dyRoadR = delta_yRoad
# Running Constraint
#cl_running = np.concatenate([-1*np.ones(N), 0*np.ones(N)])
#cu_running = np.concatenate([ 0*np.ones(N), 1*np.ones(N)])
cl_running = np.concatenate([-100 * np.ones(N), 0 * np.ones(N)])
cu_running = np.concatenate([0 * np.ones(N), 100 * np.ones(N)])
cl_tmp1 = np.concatenate([cl_running, [-lataccel_max]])
cu_tmp1 = np.concatenate([cu_running, [+lataccel_max]])
# if ns == 6:
if ns_option == 1:
# Speed Constraint
cl_tmp2 = np.concatenate([cl_tmp1, [lb_V]])
cu_tmp2 = np.concatenate([cu_tmp1, [ub_V]])
# Terminal Constraint
cl_tmp3 = np.concatenate([cl_tmp2, [-dyRoadL]])
cu_tmp3 = np.concatenate([cu_tmp2, [dyRoadR]])
cl = np.concatenate([cl_tmp3, [-delta_V + V_cmd]])
cu = np.concatenate([cu_tmp3, [delta_V + V_cmd]])
elif ns_option == 2:
# Terminal Constraint
cl_tmp3 = np.concatenate([cl_tmp1, [-dyRoadL]])
cu_tmp3 = np.concatenate([cu_tmp1, [dyRoadR]])
cl = np.concatenate([cl_tmp3, [-delta_V + V_cmd]])
cu = np.concatenate([cu_tmp3, [delta_V + V_cmd]])
elif ns_option == 3:
# Terminal Constraint
cl = np.concatenate([cl_tmp1, [-dyRoadL]])
cu = np.concatenate([cu_tmp1, [dyRoadR]])
# elif ns == 4:
#
# # Terminal Constraint
# cl = np.concatenate([cl_tmp1,[-dyRoadL]])
# cu = np.concatenate([cu_tmp1,[ dyRoadR]])
if ncons != len(cl) and ncons != len(cu):
print('Error: resolve number of constraints')
nlp = ipopt.problem(n=nu * N,
m=len(cl),
problem_obj=nlpProb(N, T, t0, x0, ncons, nu, lanes,
obstacle, posIdx, ns_option),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
nlp.addOption('print_level', nlpPrintLevel)
nlp.addOption('max_iter', nlpMaxIter)
#nlp.addOption('dual_inf_tol',10.0) # defaut = 1
nlp.addOption('constr_viol_tol', 0.1) # default = 1e-4
nlp.addOption('compl_inf_tol', 0.1) # default = 1e-4
nlp.addOption('acceptable_tol', 0.1) # default = 0.01
nlp.addOption('acceptable_constr_viol_tol', 0.1) # default = 0.01
return nlp
def rw_ipopt(wh, xmat, targets, xlb, xub, crange, max_iter, ccgoal, objgoal,
quiet):
r"""
Build and solve the reweighting NLP.
Good general settings seem to be:
get_ccscale - use ccgoal=1, method='mean'
get_objscale - use xbase=1.2, objgoal=100
no other options set, besides obvious ones
Important resources:
https://pythonhosted.org/ipopt/reference.html#reference
https://coin-or.github.io/Ipopt/OPTIONS.html
..\cyipopt\ipopt\ipopt_wrapper.py to see code from cyipopt author
Parameters
----------
wh : float
DESCRIPTION.
xmat : ndarray
DESCRIPTION.
targets : ndarray
DESCRIPTION.
xlb : TYPE, optional
DESCRIPTION. The default is 0.1.
xub : TYPE, optional
DESCRIPTION. The default is 100.
crange : TYPE, optional
DESCRIPTION. The default is .03.
max_iter : TYPE, optional
DESCRIPTION. The default is 100.
ccgoal : TYPE, optional
DESCRIPTION. The default is 1.
objgoal : TYPE, optional
DESCRIPTION. The default is 100.
quiet : TYPE, optional
DESCRIPTION. The default is True.
Returns
-------
x : TYPE
DESCRIPTION.
info : TYPE
DESCRIPTION.
"""
n = xmat.shape[0]
m = xmat.shape[1]
# xlb = 0.1
# xub = 100
# crange = .03
# max_iter = 100
# ccgoal = 1
# objgoal = 100
# quiet = True
# constraint coefficients (constant)
cc = (xmat.T * wh).T
# scale constraint coefficients and targets
ccscale = get_ccscale(cc, ccgoal=ccgoal, method='mean')
# ccscale = 1
cc = cc * ccscale # mult by scale to have avg derivative meet our goal
# print(targets)
# targets_scaled = targets.copy() * ccscale # djb do I need to copy?
print(ccscale)
targets_scaled = targets * ccscale # djb do I need to copy?
# print(targets_scaled)
# IMPORTANT: define callbacks AFTER we have scaled cc and targets
# because callbacks must be initialized with scaled cc
callbacks = Reweight_callbacks(cc, quiet)
# x vector starting values, and lower and upper bounds
x0 = np.ones(n)
lb = np.full(n, xlb)
ub = np.full(n, xub)
# constraint lower and upper bounds
cl = targets_scaled - abs(targets_scaled) * crange
cu = targets_scaled + abs(targets_scaled) * crange
nlp = ipopt.problem(n=n,
m=m,
problem_obj=callbacks,
lb=lb,
ub=ub,
cl=cl,
cu=cu)
# objective function scaling
# djb should I pass n and callbacks???
objscale = get_objscale(objgoal=objgoal,
xbase=1.2,
n=n,
callbacks=callbacks)
# print(objscale)
nlp.addOption('obj_scaling_factor', objscale) # multiplier
# define additional options as a dict
opts = {
'print_level': 5,
'file_print_level': 5,
'jac_d_constant': 'yes',
'hessian_constant': 'yes',
'max_iter': max_iter,
'mumps_mem_percent': 100, # default 1000
'linear_solver': 'mumps'
}
# TODO: check against already set options, etc. see ipopt_wrapper.py
for option, value in opts.items():
nlp.addOption(option, value)
if (not quiet):
print(f'\n {"":10} Iter {"":25} obj {"":22} infeas')
x, info = nlp.solve(x0)
return x, info
def __init__(self, rf):
self.rf = rf
def objective(self, x):
return self.rf.j(x)
def gradient(self, x):
return self.rf.dj(x, forget=False)
def constraints(self, x):
return [sum(x)]
def jacobian(self, x):
return [[1] * len(x)]
m0 = rf.initial_control()
nlp = ipopt.problem(n=len(m0),
m=0,
problem_obj=Problem(rf),
lb=[0] * len(m0),
ub=[1] * len(m0),
cl=[0],
cu=[1000])
nlp.addOption("hessian_approximation", "limited-memory")
nlp.solve(m0)
#maximize(rf, method = "SLSQP", options={'maxiter':100}, bounds=[0, 1], constraints={"type": "ineq", "fun": maxint, "jac": maxint_prime})
rf = ReducedFunctional(config, scale=-1e-6)
class Problem(object):
def __init__(self, rf):
self.rf = rf
def objective(self, x):
return self.rf.j(x)
def gradient(self, x):
return self.rf.dj(x, forget=False)
def constraints(self, x):
return [sum(x)]
def jacobian(self, x):
return [[1]*len(x)]
m0 = rf.initial_control()
nlp = ipopt.problem(n=len(m0),
m=0,
problem_obj=Problem(rf),
lb=[0]*len(m0),
ub=[1]*len(m0),
cl=[0],
cu=[1000])
nlp.addOption("hessian_approximation", "limited-memory")
nlp.solve(m0)
def solve(self, prob):
"""
Solve a non-linear problem
Parameters
----------
prob : pycalphad.core.problem.Problem
Returns
-------
SolverResult
"""
cur_conds = prob.conditions
comps = prob.pure_elements
nlp = ipopt.problem(
n=prob.num_vars,
m=prob.num_constraints,
problem_obj=prob,
lb=prob.xl,
ub=prob.xu,
cl=prob.cl,
cu=prob.cu
)
self.apply_options(nlp)
length_scale = np.min(np.abs(prob.cl))
length_scale = max(length_scale, 1e-9)
# Note: Using the ipopt derivative checker can be tricky at the edges of composition space
# It will not give valid results for the finite difference approximation
x, info = nlp.solve(prob.x0)
dual_inf = np.max(np.abs(info['mult_g']*info['g']))
if dual_inf > self.infeasibility_threshold:
if self.verbose:
print('Trying to improve poor solution')
# Constraints are getting tiny; need to be strict about bounds
if length_scale < 1e-6:
nlp.addOption(b'compl_inf_tol', 1e-3 * float(length_scale))
nlp.addOption(b'bound_relax_factor', MIN_SITE_FRACTION)
# This option ensures any bounds failures will fail "loudly"
# Otherwise we are liable to have subtle mass balance errors
nlp.addOption(b'honor_original_bounds', b'no')
else:
nlp.addOption(b'dual_inf_tol', self.infeasibility_threshold)
accurate_x, accurate_info = nlp.solve(x)
if accurate_info['status'] >= 0:
x, info = accurate_x, accurate_info
chemical_potentials = prob.chemical_potentials(x)
if info['status'] == -10:
# Not enough degrees of freedom; nothing to do
if len(prob.composition_sets) == 1:
converged = True
chemical_potentials[:] = prob.composition_sets[0].energy
else:
converged = False
elif info['status'] < 0:
if self.verbose:
print('Calculation Failed: ', cur_conds, info['status_msg'])
converged = False
else:
converged = True
if self.verbose:
print('Chemical Potentials', chemical_potentials)
print(info['mult_x_L'])
print(x)
print('Status:', info['status'], info['status_msg'])
return SolverResult(converged=converged, x=x, chemical_potentials=chemical_potentials)
def main():
global x_goal
global y_goal
global w_goal
w_curr1 = check_omega(w_goal)
# Define the problem constraints on the optimized variable vector
x0 = [0, 0, 0, 0, 0, 0]
lb = [0, -3, -3, -3, -3, -3]
ub = [8, 3, 3, 3, 3, 3]
#define the inequality constraints limit
cl = [-0.418, -0.428, -0.428, -0.428, abs(w_goal - w_curr1), -0.4, 0, 0]
cu = [0.428, 0.428, 0.428, 0.428, abs(w_goal - w_curr1), +0.4, 100, 100]
#define the non linear optimization problem
nlp = ipopt.problem(n=len(x0),
m=len(cl),
problem_obj=hs071(),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
#
# Set solver options
#
#nlp.addOption('derivative_test', 'second-order')
nlp.addOption(b'mu_strategy', b'adaptive')
nlp.addOption(b'tol', 1e-7)
#
# Scale the problem (Just for demonstration purposes)
#
nlp.setProblemScaling(obj_scaling=2, x_scaling=[1, 1, 1, 1, 1, 1])
nlp.addOption(b'nlp_scaling_method', b'user-scaling')
#
# Solve the problem
#
x, info = nlp.solve(x0)
# print("Solution of the primal variables: x=%s\n" % repr(x))
vel = float(x[0])
omega = float(x[1])
vel_msg.x = vel
vel_msg.y = omega
vel_msg.z = w_curr1
#publish the message containing the velocity commands.
pub.publish(vel_msg)
goal_msg.x = x_goal
goal_msg.y = y_goal
goal_msg.z = w_goal
pub2.publish(goal_msg)
if vel < 0.5:
vel = 0.5
drive_msg.velocity = vel
drive_msg.angle = omega * 1.5
pub3.publish(drive_msg)
def intermediate(self, alg_mod, iter_count, obj_value, inf_pr, inf_du, mu,
d_norm, regularization_size, alpha_du, alpha_pr,
ls_trials):
#
# Example for the use of the intermediate callback.
#
print("Objective value at iteration ", iter_count, " is -", obj_value)
x0 = [1.0, 5.0, 5.0, 1.0]
lb = [1.0, 1.0, 1.0, 1.0]
ub = [5.0, 5.0, 5.0, 5.0]
cl = [25.0, 40.0]
cu = [2.0e19, 40.0]
nlp = ipopt.problem(n=len(x0),
m=len(cl),
problem_obj=hs071(),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
nlp.addOption('mu_strategy', 'adaptive')
nlp.addOption('tol', 1e-7)
x, info = nlp.solve(x0)
# constants_dict['xd'] = (0.9 + 0.2*rand_list[11])*constants_dict['xd'] # ShankInertia
# constants_dict['yd'] = (0.9 + 0.2*rand_list[12])*constants_dict['yd'] # ShankInertia
nlp = ipopt.problem(n=num_states * num_nodes + num_nodes * num_open +
num_normal * num_control + num_par,
m=num_cons * (num_nodes - 1),
problem_obj=gait2dpi_model(x_c_meas_vec,
x_normal_ref,
vs_meas,
num_nodes,
num_states,
index_open,
index_control,
num_normal,
num_par,
interval,
weight_mt,
weight_ms,
weight_ts,
weight_tm,
weight_tp,
index_normal,
index2_imped,
constants_dict,
scaling=Scaling),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
nlp.addOption(b'linear_solver', b'MA86')
nlp.addOption(b'hessian_approximation', b'limited-memory')
def reweight(self,
xlb=0.1,
xub=100,
crange=.03,
max_iter=100,
ccgoal=1,
objgoal=100,
quiet=True):
r"""
Build and solve the reweighting NLP.
Good general settings seem to be:
get_ccscale - use ccgoal=1, method='mean'
get_objscale - use xbase=1.2, objgoal=100
no other options set, besides obvious ones
Important resources:
https://pythonhosted.org/ipopt/reference.html#reference
https://coin-or.github.io/Ipopt/OPTIONS.html
..\cyipopt\ipopt\ipopt_wrapper.py to see code from cyipopt author
Returns
-------
x : TYPE
DESCRIPTION.
info : TYPE
DESCRIPTION.
"""
# constraint coefficients (constant)
# cc = self._xmat * self._wh[:, None]
# cc = self._xmat * self._wh
cc = (self._xmat.T * self._wh).T
# scale constraint coefficients and targets
ccscale = self.get_ccscale(cc, ccgoal=ccgoal, method='mean')
# print(ccscale)
# ccscale = 1
cc = cc * ccscale # mult by scale to have avg derivative meet our goal
targets = self._targets * ccscale
# IMPORTANT: define callbacks AFTER we have scaled cc and targets
# because callbacks must be initialized with scaled cc
callbacks = Reweight_callbacks(cc, quiet)
# x vector starting values, and lower and upper bounds
x0 = np.ones(self._n)
lb = np.full(self._n, xlb)
ub = np.full(self._n, xub)
# constraint lower and upper bounds
cl = targets - abs(targets) * crange
cu = targets + abs(targets) * crange
nlp = ipopt.problem(n=self._n,
m=self._m,
problem_obj=self.callbacks,
lb=lb,
ub=ub,
cl=cl,
cu=cu)
# objective function scaling
objscale = self.get_objscale(objgoal=objgoal, xbase=1.2)
# print(objscale)
nlp.addOption('obj_scaling_factor', objscale) # multiplier
# define additional options as a dict
opts = {
'print_level': 5,
'file_print_level': 5,
'jac_d_constant': 'yes',
'hessian_constant': 'yes',
'max_iter': max_iter,
'mumps_mem_percent': 100, # default 1000
'linear_solver': 'mumps',
}
# TODO: check against already set options, etc. see ipopt_wrapper.py
for option, value in opts.items():
nlp.addOption(option, value)
# outfile = 'test4.out'
# if os.path.exists(outfile):
# os.remove(outfile)
# nlp.addOption('output_file', outfile)
# nlp.addOption('derivative_test', 'first-order') # second-order
# nlp_scaling_method: default gradient-based
# equilibration-based needs MC19
# nlp.addOption('nlp_scaling_method', 'equilibration-based')
# nlp.addOption('nlp_scaling_max_gradient', 1e-4) # 100 default
# nlp.addOption('mu_strategy', 'adaptive') # not good
# nlp.addOption('mehrotra_algorithm', 'yes') # not good
# nlp.addOption('mumps_mem_percent', 100) # default 1000
# nlp.addOption('mumps_pivtol', 1e-4) # default 1e-6; 1e-2 is SLOW
# nlp.addOption('mumps_scaling', 8) # 77 default
x, info = nlp.solve(x0)
return x, info
def bilinear_formulation():
''' Definition the bilinear problem '''
math = Math()
pos0 = np.array([2.5, 2.5, 2.5])
force0 = np.array([-10.0, 10.0, 10.0])
posDesired = np.array([2.0, 2.0, 2.0])
forceDesired = np.array([0.0, 0.0, 10.0])
tauDesired = np.dot(math.skew(posDesired), forceDesired)
print "initial contact position: ", pos0
print "initial force: ", force0
print "Desired torque: ", tauDesired
x0 = np.hstack([pos0, force0])
lb = [1.0, 1.0, 1.0, -20.0, -20.0, 0.0]
ub = [3.0, 3.0, 3.0, 20.0, 20.0, 20.0]
tau_x_constraints_lb = np.array([20.0])
tau_x_constraints_ub = np.array([20.0])
tau_y_constraints_lb = np.array([-20.0])
tau_y_constraints_ub = np.array([-20.0])
tau_z_constraints_lb = np.array([0.0])
tau_z_constraints_ub = np.array([0.0])
cl = np.hstack(
[tau_x_constraints_lb, tau_y_constraints_lb, tau_z_constraints_lb])
cu = np.hstack(
[tau_x_constraints_ub, tau_y_constraints_ub, tau_z_constraints_ub])
nlpConvex = ipopt.problem(n=len(x0),
m=len(cl),
problem_obj=buildBilinearFormulation(x0),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
#
# Set solver options
#
#nlp.addOption('derivative_test', 'second-order')
nlpConvex.addOption('mu_strategy', 'adaptive')
nlpConvex.addOption('jacobian_approximation', 'finite-difference-values')
nlpConvex.addOption('hessian_approximation', 'limited-memory')
nlpConvex.addOption('tol', 1e-2)
#
# Scale the problem (Just for demonstration purposes)
#
nlpConvex.setProblemScaling(obj_scaling=1,
x_scaling=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
nlpConvex.addOption('nlp_scaling_method', 'user-scaling')
#
# Solve the problem
#
sol, info = nlpConvex.solve(x0)
print "Solution of the primal variables: x=%s\n" % repr(sol)
print "Solution of the dual variables: lambda=%s\n" % repr(info['mult_g'])
print "Objective=%s\n" % repr(info['obj_val'])
l = sol[0:3]
f = sol[3:6]
tau = np.dot(math.skew(l), f)
print colored('-----------> Results check: ', 'red')
print colored('desired torque: ',
'blue'), tauDesired[0], tauDesired[1], tauDesired[2]
print colored('actual torque: ', 'green'), tau
print colored('torque error: ', 'red'), np.subtract(tau, tauDesired)
print colored('foot pos: ', 'green'), l
print colored('force: ', 'green'), f
def jacobian(self, xi):
r, c, d = c_x(xi)
return coo_matrix((d, (r, c))).toarray()
x0 = nlp_x
lb = [-1e19] * len(x0)
ub = [ 1e19] * len(x0)
cl = [0, 0, 0] + [0, 0, 0, 0] * 10 + [0] + [0, 0]
cu = [0, 0, 0] + [1e19, 0, 0, 0] * 10 + [1e19] + [0, 0]
nlp = ipopt.problem(
n=len(x0),
m=len(cl),
problem_obj=stryk(),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
#
# Set solver options
#
#nlp.addOption('derivative_test', 'second-order')
nlp.addOption('mu_strategy', 'adaptive')
nlp.addOption('tol', 1e-7)
#
# Scale the problem (Just for demonstration purposes)
#
def linear_mpc_control4(xref, xbar, z0, dref):
z0 = z0[:NX]
# [:-4] 是去除最后一个点，保留T个点
zref = np.concatenate((z0, xref.reshape(-1, 1, order='F')[:, 0][:-8]))
x0 = zref.tolist()
# lb = [1.0, 1.0, 1.0, 1.0]
# ub = [5.0, 5.0, 5.0, 5.0]
# cl = [25.0, 40.0]
# cu = [2.0e19, 40.0]
ub = z0.copy()
lb = z0.copy()
cl = [0 for i in range((T - 1) * NX)]
cu = [0 for i in range((T - 1) * NX)]
'''
x0 is the initial value of the variable to be solved
'''
# v上限下限
for i in range(T - 1):
ub.extend([10e9, 10e9, MAX_SPEED, 10e9])
lb.extend([-10e9, -10e9, MIN_SPEED, -10e9])
# x0 += [0,0,0,0]
# a和steer上限下限
for i in range(T - 1):
ub.extend([MAX_ACCEL, MAX_STEER])
lb.extend([-MAX_ACCEL, -MAX_STEER])
x0.extend([0, 0])
# dsteer上下限
for i in range(T - 2):
cl.append(-DT * MAX_DSTEER)
cu.append(DT * MAX_DSTEER)
# print("x is",len(x0))
nlp = ipopt.problem(n=len(x0),
m=len(cl),
problem_obj=nlmpc_problem.nlmpc(zref, T),
lb=lb,
ub=ub,
cl=cl,
cu=cu)
nlp.addOption('mu_strategy', 'adaptive')
nlp.addOption('tol', 1e-3)
nlp.addOption('print_level', 0)
nlp.addOption("max_iter", 100)
nlp.addOption("warm_start_init_point", "yes")
# nlp.addOption("mehrotra_algorithm", "yes")
x, info = nlp.solve(x0)
z = x[:T * NX]
u = x[T * NX:]
ox = z[0::NX]
oy = z[1::NX]
ov = z[2::NX]
oyaw = z[3::NX]
oa = u[0::2]
od = u[1::2]
# print("z0 is\n", z0)
# print("lb is\n", lb)
# print("ub is\n", ub )
# print("zref is\n", zref)
# print(z)
# print(u)
return oa, od, ox, oy, oyaw, ov
def runstub(AGI_STUB, tolerances, nzcc, pufbase_state, log_dir, interim_results_dir):
stub = AGI_STUB
constraint_value = 1000 # each constraint will be this number when scaled
constraints_unscaled = tolerances[tolerances.AGI_STUB == stub].target
constraint_scales = np.where(constraints_unscaled == 0, 1, abs(constraints_unscaled) / constraint_value)
constraints = constraints_unscaled / constraint_scales
# constraints
# create nzcc for the stub, and on each record create i to index constraints and j to index variables (the x elements)
nzcc_stub = nzcc[nzcc.AGI_STUB == stub].sort_values(by = ['constraint_name', 'RECID'])
# NOTE!!: create i and j, each of which will be consecutive integersm where
# i gives the index for constraints
# j gives the index for the RECID (for the variables)
# TODO: There should be better ways to do this in python (like grouop_indeces in R)
# Note that the indeces start from 0 here in python. (start from 1 in R)
#nzcc_stub
nzcc_stub['i'] = nzcc_stub.constraint_name
nzcc_stub['j'] = nzcc_stub.RECID
nzcc_stub.set_index(['i', 'j'], inplace = True)
rename_dic1 = dict(zip(nzcc_stub.constraint_name.unique(), list(range(len(nzcc_stub.constraint_name.unique())))))
rename_dic2 = dict(zip(np.sort(nzcc_stub.RECID.unique()), list(range(len(nzcc_stub.RECID.unique())))))
nzcc_stub.rename(index = rename_dic1, level = 'i', inplace = True)
nzcc_stub.rename(index = rename_dic2, level = 'j', inplace = True)
nzcc_stub.reset_index(inplace = True)
# nzcc_stub[nzcc_stub.i == 1].loc[:, ['i', 'j', 'constraint_name', 'RECID']]
nzcc_stub['nzcc_unscaled'] = nzcc_stub.nzcc
nzcc_stub['nzcc'] = nzcc_stub.nzcc_unscaled / np.take(constraint_scales, nzcc_stub.i)
# Inputs
inputs = {}
inputs['p'] = 2
inputs['wt'] = pufbase_state[pufbase_state.AGI_STUB == stub].sort_values(by = 'RECID').weight_initial # note this is a pd.Series
inputs['RECID'] = pufbase_state[pufbase_state.AGI_STUB == stub].sort_values(by = 'RECID').RECID
inputs['constraint_coefficients_sparse'] = nzcc_stub
inputs['n_variables'] = len(inputs['wt'])
inputs['n_constraints'] = len(constraints)
inputs['objscale'] = 1e6 # scaling constant used in the various objective function functions
inputs['constraint_scales'] = constraint_scales
xlb = np.repeat(0, inputs['n_variables']) # arbitrary
xub = np.repeat(100, inputs['n_variables']) # arbitrary
x0 = np.repeat(1, inputs['n_variables'])
tol = tolerances[tolerances.AGI_STUB == stub].tol_default
clb = constraints - abs(constraints) * tol
cub = constraints + abs(constraints) * tol
clb.fillna(0, inplace = True)
cub.fillna(0, inplace = True)
nlp_puf = ipopt.problem(
n=len(x0),
m=len(clb),
problem_obj=puf_obj(inputs),
lb=xlb,
ub=xub,
cl=clb,
cu=cub
)
logfile_name = log_dir + "stub_" + str(stub) + ".out"
nlp_puf.addOption('print_level', 0)
nlp_puf.addOption('file_print_level', 5)
# nlp_puf.addOption('linear_solver', 'ma27') # cyipopt uses MUMPS solver as its default solver
# TODO: Try other solvers
nlp_puf.addOption('max_iter', 100)
nlp_puf.addOption('mu_strategy', 'adaptive')
nlp_puf.addOption('output_file', logfile_name)
x, info = nlp_puf.solve(x0)
print(info['status_msg'])
return pd.DataFrame({'AGI_STUB' : stub, 'RECID': inputs['RECID'], 'wt_int' : inputs['wt'], 'x' : x})
