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 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
# Create the initial guess for the current optimization problem
mission['numerics']['iterationSchedule']['curIter'] = curIter
if curIter == 0:
mission = LDP.processAllMissionData(mission)
vars0 = IC.CreateInitialGuess(mission)
else:
mission, vars0 = postProcessing.UpdateMissionAndInitialGuess(mission)
#vars0 = IC.UpdateInitialGuess(mission)
numEquality = len(constraints.equality(vars0, mission))
numInequality = len(constraints.inequality(vars0, mission))
# Find the upper and lower bounds
#boundsCase = constraints.bounds(mission)
lb, ub = constraints.getXLXU(vars0, mission)
#TJC
opt_prob = pyOpt.Optimization('Trajectory Optimization',
lambda x: objectiveFunction(x, mission))
opt_prob.addObj('Objective Mass')
# Specify all of the variables
print 'Setting up variables in a hackish way. MUST CHANGE!!!'
for curVar in range(len(vars0)):
opt_prob.addVar('var' + str(curVar),
'c',
value=vars0[curVar],
lower=lb[curVar],
upper=ub[curVar])
def SNOPTrun(vars0, mission, includeDrag, flagFORCE=1):
import pyOpt
import constraints
import costFunction
import numpy as np
# THESE FUNCTIONS ARE USED BY SNOPT
# Define the functions SNOPT optimizer will call
def objectiveFunction(inVars, mission, includeDrag):
x = costFunction.ObjectiveMass(inVars, mission)
eq = constraints.equality(inVars, mission, 'real', includeDrag)
ineq = constraints.inequality(inVars, mission, 'real')
g = np.concatenate((eq, ineq), 1)
fail = 0
return x, g, fail
def sensitivityFunction(inVars, f, g, mission, includeDrag):
x = costFunction.fprimeObjectiveMass(inVars, mission)
eq = constraints.fprimeequality(inVars, mission, '2d', includeDrag)
ineq = constraints.fprimeinequality(inVars, mission, '2d')
g = np.concatenate((eq, ineq), 0)
fail = 0
return x, g, fail
numEquality = len(constraints.equality(vars0, mission))
numInequality = len(constraints.inequality(vars0, mission))
# Find the upper and lower bounds
#boundsCase = constraints.bounds(mission)
lb, ub = constraints.getXLXU(vars0, mission)
#TJC
opt_prob = pyOpt.Optimization(
'Trajectory Optimization',
lambda x: objectiveFunction(x, mission, includeDrag))
opt_prob.addObj('Objective Mass')
# Specify all of the variables
#print 'Setting up variables in a hackish way. MUST CHANGE!!!'
for curVar in range(len(vars0)):
opt_prob.addVar('var' + str(curVar),
'c',
value=vars0[curVar],
lower=lb[curVar],
upper=ub[curVar])
# Now add in equality constraints
for curCon in range(numEquality):
opt_prob.addCon('g' + str(curCon), 'e')
# Now add in inequality constraints
for curCon in range(numEquality, numEquality + numInequality):
opt_prob.addCon('g' + str(curCon), 'i')
# Confirm that everything is correct
#print opt_prob
# Set up the optimizer
snopt = pyOpt.pySNOPT.SNOPT()
snopt.setOption('Major feasibility tolerance', value=5e-6)
snopt.setOption('Major optimality tolerance', value=1e-5)
snopt.setOption('Minor feasibility tolerance', value=5e-6)
snopt.setOption('Major iterations limit', 500)
print 'Using SNOPT'
# Optimize and save results
sens2 = lambda x, f, g: sensitivityFunction(x, f, g, mission, includeDrag)
# by default will try complex step first...if fails...then finite diference
exitVal = snopt(opt_prob, sens_type=sens2)
infoOpt = exitVal[2]['text']
if infoOpt != 'finished successfully' and flagFORCE == 1:
print 'Failed to finish successfully with CS .... trying FD'
exitVal = snopt(opt_prob, sens_type='FD')
return exitVal
def SNOPTrun(vars0,mission,includeDrag,flagFORCE=1):
import pyOpt
import constraints
import costFunction
import numpy as np
# THESE FUNCTIONS ARE USED BY SNOPT
# Define the functions SNOPT optimizer will call
def objectiveFunction(inVars,mission,includeDrag):
x=costFunction.ObjectiveMass(inVars,mission)
eq=constraints.equality(inVars,mission,'real',includeDrag)
ineq=constraints.inequality(inVars,mission,'real')
g = np.concatenate((eq,ineq),1)
fail = 0
return x,g,fail
def sensitivityFunction(inVars,f,g,mission,includeDrag):
x = costFunction.fprimeObjectiveMass(inVars,mission)
eq = constraints.fprimeequality(inVars,mission,'2d',includeDrag)
ineq = constraints.fprimeinequality(inVars,mission,'2d')
g = np.concatenate((eq,ineq),0)
fail = 0
return x,g,fail
numEquality = len(constraints.equality(vars0,mission))
numInequality = len(constraints.inequality(vars0,mission))
# Find the upper and lower bounds
#boundsCase = constraints.bounds(mission)
lb,ub = constraints.getXLXU(vars0,mission)
#TJC
opt_prob = pyOpt.Optimization('Trajectory Optimization',lambda x: objectiveFunction(x,mission,includeDrag))
opt_prob.addObj('Objective Mass')
# Specify all of the variables
#print 'Setting up variables in a hackish way. MUST CHANGE!!!'
for curVar in range(len(vars0)):
opt_prob.addVar('var'+str(curVar), 'c', value=vars0[curVar], lower=lb[curVar], upper=ub[curVar])
# Now add in equality constraints
for curCon in range(numEquality):
opt_prob.addCon('g' + str(curCon), 'e')
# Now add in inequality constraints
for curCon in range(numEquality,numEquality + numInequality):
opt_prob.addCon('g' + str(curCon), 'i')
# Confirm that everything is correct
#print opt_prob
# Set up the optimizer
snopt = pyOpt.pySNOPT.SNOPT()
snopt.setOption('Major feasibility tolerance',value=5e-6)
snopt.setOption('Major optimality tolerance',value=1e-5)
snopt.setOption('Minor feasibility tolerance',value=5e-6)
snopt.setOption('Major iterations limit',500)
print 'Using SNOPT'
# Optimize and save results
sens2 = lambda x,f,g:sensitivityFunction(x,f,g,mission,includeDrag)
# by default will try complex step first...if fails...then finite diference
exitVal = snopt(opt_prob,sens_type= sens2)
infoOpt = exitVal[2]['text']
if infoOpt!='finished successfully' and flagFORCE==1:
print 'Failed to finish successfully with CS .... trying FD'
exitVal = snopt(opt_prob,sens_type= 'FD')
return exitVal