def setIdasIntegrator(self, integratorOptions=[]):
# make dae input fun
daeSXFun = self.dae.sxFun()
assert self.nStates()==daeSXFun.inputSX(C.DAE_X).size()
assert self.nActions()+self.nParams()==daeSXFun.inputSX(C.DAE_P).size()
# make integrator
self.integrator = C.IdasIntegrator(daeSXFun)
setFXOptions(self.integrator, integratorOptions)
self.integrator.init()
# set up dynamics constraints
for k in range(0,self.nSteps-1):
uk = self.actions[:,k]
ukp1 = self.actions[:,k+1]
xk = self.states[:,k]
xkp1 = self.states[:,k+1]
p = self.params
upk = C.veccat([uk,p])
self.addConstraint(self.integrator.call([xk,upk])[C.INTEGRATOR_XF],'==',xkp1)
def setIdasIntegrator(self, integratorOptions=[]):
# make dae input fun
daeSXFun = self.dae.sxFun()
assert self.nStates()==daeSXFun.inputSX(C.DAE_X).size()
assert self.nActions()+self.nParams()==daeSXFun.inputSX(C.DAE_P).size()
# make integrator
self.integrator = C.IdasIntegrator(daeSXFun)
setFXOptions(self.integrator, integratorOptions)
self.integrator.init()
# set up dynamics constraints
for k in range(0,self.nSteps-1):
uk = self.actions[:,k]
ukp1 = self.actions[:,k+1]
xk = self.states[:,k]
xkp1 = self.states[:,k+1]
p = self.params
upk = C.veccat([uk,p])
self.addConstraint(self.integrator.call([xk,upk])[C.INTEGRATOR_XF],'==',xkp1)
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()
# Nonlinear constraint function
gfcn = CS.MXFunction([self._V],[g])
setFXOptions(gfcn,constraintFunOpts)
# Objective function of the NLP
if not hasattr(self,'_objective'):
raise ValueError('need to set objective function')
ofcn = CS.MXFunction([self._V],[self._objective])
# solver callback (optional)
if callback is not None:
nd = self._V.size()
nc = self._constraints.getG().size()
c = CS.PyFunction( callback, CS.nlpsolverOut(x_opt=CS.sp_dense(nd,1), cost=CS.sp_dense(1,1), lambda_x=CS.sp_dense(nd,1), lambda_g = CS.sp_dense(nc,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(ofcn,gfcn)
# Set options
setFXOptions(self.solver, solverOpts)
# initialize the solver
self.solver.init()
# Bounds on g
self.solver.setInput(lbg,CS.NLP_LBG)
self.solver.setInput(ubg,CS.NLP_UBG)
def setSolver(self, solver, solverOptions=[], objFunOptions=[], constraintFunOptions=[]):
if hasattr(self, '_solver'):
raise ValueError("You've already set a solver and you can't change it")
if not hasattr(self, '_objective'):
raise ValueError("You need to set an objective")
# make objective function
f = C.MXFunction([self.getDesignVars()], [self._objective])
setFXOptions(f, objFunOptions)
f.init()
# make constraint function
g = C.MXFunction([self.getDesignVars()], [self._constraints.getG()])
setFXOptions(g, constraintFunOptions)
g.init()
def mkParallelG():
gs = [C.MXFunction([self.getDesignVars()],[gg]) for gg in self._constraints._g]
for gg in gs:
gg.init()
pg = C.Parallelizer(gs)
# pg.setOption("parallelization","openmp")
pg.setOption("parallelization","serial")
# pg.setOption("parallelization","expand")
pg.init()
dvsDummy = C.msym('dvs',(self.nStates()+self.nActions())*self.nSteps+self.nParams())
g_ = C.MXFunction([dvsDummy],[C.veccat(pg.call([dvsDummy]*len(gs)))])
g_.init()
return g_
# parallelG = mkParallelG()
# guess = self._initialGuess.vectorize()
# parallelG.setInput([x*1.1 for x in guess])
# g.setInput([x*1.1 for x in guess])
#
# g.evaluate()
# parallelG.evaluate()
# print parallelG.output()-g.output()
# exit(0)
# make solver function
# self._solver = solver(f, mkParallelG())
self._solver = solver(f, g)
setFXOptions(self._solver, solverOptions)
self._solver.init()
# set constraints
self._solver.setInput(self._constraints.getLb(), C.NLP_LBG)
self._solver.setInput(self._constraints.getUb(), C.NLP_UBG)
self.setBounds()
def setSolver(self, solver, solverOptions=[], objFunOptions=[], constraintFunOptions=[]):
if hasattr(self, '_solver'):
raise ValueError("You've already set a solver and you can't change it")
if not hasattr(self, '_objective'):
raise ValueError("You need to set an objective")
# make objective function
f = C.MXFunction([self.getDesignVars()], [self._objective])
setFXOptions(f, objFunOptions)
f.init()
# make constraint function
g = C.MXFunction([self.getDesignVars()], [self._constraints.getG()])
setFXOptions(g, constraintFunOptions)
g.init()
def mkParallelG():
gs = [C.MXFunction([self.getDesignVars()],[gg]) for gg in self._constraints._g]
for gg in gs:
gg.init()
pg = C.Parallelizer(gs)
# pg.setOption("parallelization","openmp")
pg.setOption("parallelization","serial")
# pg.setOption("parallelization","expand")
pg.init()
dvsDummy = C.msym('dvs',(self.nStates()+self.nActions())*self.nSteps+self.nParams())
g_ = C.MXFunction([dvsDummy],[C.veccat(pg.call([dvsDummy]*len(gs)))])
g_.init()
return g_
# parallelG = mkParallelG()
# guess = self._initialGuess.vectorize()
# parallelG.setInput([x*1.1 for x in guess])
# g.setInput([x*1.1 for x in guess])
#
# g.evaluate()
# parallelG.evaluate()
# print parallelG.output()-g.output()
# exit(0)
# make solver function
# self._solver = solver(f, mkParallelG())
self._solver = solver(f, g)
setFXOptions(self._solver, solverOptions)
self._solver.init()
# set constraints
self._solver.setInput(self._constraints.getLb(), C.NLP_LBG)
self._solver.setInput(self._constraints.getUb(), C.NLP_UBG)
self.setBounds()
