def setupSolver(self, solverOpts=[], constraintFunOpts=[], callback=None):
if not self.collocationIsSetup:
raise ValueError("you forgot to call setupCollocation")
g = self._constraints.getG()
lbg = self._constraints.getLb()
ubg = self._constraints.getUb()
# Objective function/constraints of the NLP
if not hasattr(self, '_objective'):
raise ValueError('need to set objective function')
nlp = CS.MXFunction(CS.nlpIn(x=self._dvMap.vectorize()),
CS.nlpOut(f=self._objective, g=g))
setFXOptions(nlp, constraintFunOpts)
nlp.init()
# solver callback (optional)
if callback is not None:
nd = self._dvMap.vectorize().size()
nc = self._constraints.getG().size()
c = CS.PyFunction(
callback,
CS.nlpSolverOut(x=CS.sp_dense(nd, 1),
f=CS.sp_dense(1, 1),
lam_x=CS.sp_dense(nd, 1),
lam_g=CS.sp_dense(nc, 1),
lam_p=CS.sp_dense(0, 1),
g=CS.sp_dense(nc, 1)), [CS.sp_dense(1, 1)])
c.init()
solverOpts.append(("iteration_callback", c))
# Allocate an NLP solver
self.solver = CS.IpoptSolver(nlp)
# self.solver = CS.WorhpSolver(nlp)
# self.solver = CS.SQPMethod(nlp)
# Set options
setFXOptions(self.solver, solverOpts)
# initialize the solver
self.solver.init()
# Bounds on g
self.solver.setInput(lbg, 'lbg')
self.solver.setInput(ubg, 'ubg')
## Nonlinear constraint function, for debugging
gfcn = CS.MXFunction([self._dvMap.vectorize()], [g])
gfcn.init()
setFXOptions(gfcn, constraintFunOpts)
self._gfcn = gfcn
def setupSolver(self,solverOpts=[],constraintFunOpts=[],callback=None):
if not self.collocationIsSetup:
raise ValueError("you forgot to call setupCollocation")
g = self._constraints.getG()
lbg = self._constraints.getLb()
ubg = self._constraints.getUb()
# Objective function/constraints of the NLP
if not hasattr(self,'_objective'):
raise ValueError('need to set objective function')
nlp = CS.MXFunction(CS.nlpIn(x=self._dvMap.vectorize()),CS.nlpOut(f=self._objective, g=g))
setFXOptions(nlp,constraintFunOpts)
nlp.init()
# solver callback (optional)
if callback is not None:
nd = self._dvMap.vectorize().size()
nc = self._constraints.getG().size()
c = CS.PyFunction( callback,
CS.nlpSolverOut(x = CS.sp_dense(nd,1),
f = CS.sp_dense(1,1),
lam_x = CS.sp_dense(nd,1),
lam_g = CS.sp_dense(nc,1),
lam_p = CS.sp_dense(0,1),
g = CS.sp_dense(nc,1) ),
[CS.sp_dense(1,1)] )
c.init()
solverOpts.append( ("iteration_callback", c) )
# Allocate an NLP solver
self.solver = CS.IpoptSolver(nlp)
# self.solver = CS.WorhpSolver(nlp)
# self.solver = CS.SQPMethod(nlp)
# Set options
setFXOptions(self.solver, solverOpts)
# initialize the solver
self.solver.init()
# Bounds on g
self.solver.setInput(lbg,'lbg')
self.solver.setInput(ubg,'ubg')
## Nonlinear constraint function, for debugging
gfcn = CS.MXFunction([self._dvMap.vectorize()],[g])
gfcn.init()
setFXOptions(gfcn,constraintFunOpts)
self._gfcn = gfcn
CT.entry("sref",expr=geom.sref)])
obj = -CL / (CDi + 0.01)
nlp = C.SXFunction(C.nlpIn(x=dvs),C.nlpOut(f=obj,g=g))
# callback
class MyCallback:
def __init__(self):
self.iters = []
def __call__(self,f,*args):
self.iters.append(numpy.array(f.getInput("x")))
mycallback = MyCallback()
pyfun = C.PyFunction( mycallback, C.nlpSolverOut(x=C.sp_dense(dvs.size,1),
f=C.sp_dense(1,1),
lam_x=C.sp_dense(dvs.size,1),
lam_g = C.sp_dense(g.size,1),
lam_p = C.sp_dense(0,1),
g = C.sp_dense(g.size,1) ),
[C.sp_dense(1,1)] )
pyfun.init()
solver = C.IpoptSolver(nlp)
solver.setOption('tol',1e-11)
solver.setOption('linear_solver','ma27')
#solver.setOption('linear_solver','ma57')
solver.setOption("iteration_callback",pyfun)
solver.init()
lbg = g(solver.input("lbg"))
ubg = g(solver.input("ubg"))
lbx = dvs(solver.input("lbx"))