def __init__(self,*args):
shape = 1
if len(args)==1:
shape = args[0]
elif len(args)>1:
shape = [args[0],args[1]]
if len(args)==3:
name = args[2]
else:
name = 'p'
OptimizationObject.__init__(self,shape,name)
def optival( *args ):
if len(args)==1 and isinstance(args[0],OptimizationObject):
return np.array(args[0].optival())
symbols_struct = OptimizationObject.get_primitives(args)
symbols = []
hassymbols = False;
symbolsx = [C.MX.sym('dummy')]; # bug in casadi typemaps: [] does not work
if 'x' in symbols_struct:
symbols+=symbols_struct["x"]
hassymbols = True
if 'p' in symbols_struct:
symbols+=symbols_struct["p"]
hassymbols = True
if not hassymbols:
symbols = [MX.sym("dummy")]
f = C.Function('f',symbols,args);
f_inputs = [];
if hassymbols:
for i,e in enumerate(symbols):
f_inputs.append(optival(e))
out = f.call(f_inputs)
if len(args)==1:
return np.array(out[0])
else:
return [np.array(out[i]) for i in range(f.nOut())]
def optival(*args):
if len(args) == 1 and isinstance(args[0], OptimizationObject):
return np.array(args[0].optival())
symbols_struct = OptimizationObject.get_primitives(args)
symbols = []
hassymbols = False
symbolsx = [C.MX.sym('dummy')]
# bug in casadi typemaps: [] does not work
if 'x' in symbols_struct:
symbols += symbols_struct["x"]
hassymbols = True
if 'p' in symbols_struct:
symbols += symbols_struct["p"]
hassymbols = True
if not hassymbols:
symbols = [C.MX.sym("dummy")]
f = C.Function('f', symbols, args)
f_inputs = []
if hassymbols:
for i, e in enumerate(symbols):
f_inputs.append(optival(e))
out = f.call(f_inputs)
if len(args) == 1:
return np.array(out[0])
else:
return [np.array(out[i]) for i in range(f.nOut())]
def __init__(self, *args):
shape = 1
if len(args) == 1:
shape = args[0]
elif len(args) > 1:
shape = [args[0], args[1]]
if len(args) == 3:
name = args[2]
else:
name = 'x'
import casadi
OptimizationObject.__init__(self, shape, name)
self.lb = -casadi.inf * casadi.DM.ones(self.sparsity())
self.ub = casadi.inf * casadi.DM.ones(self.sparsity())
self.init = casadi.DM.zeros(self.sparsity())
def __init__(self,*args):
shape = 1
if len(args)==1:
shape = args[0]
elif len(args)>1:
shape = [args[0],args[1]]
if len(args)==3:
name = args[2]
else:
name = 'x'
import casadi
OptimizationObject.__init__(self,shape,name)
self.lb = -casadi.inf*casadi.DM.ones(self.sparsity())
self.ub = casadi.inf*casadi.DM.ones(self.sparsity())
self.init = casadi.DM.zeros(self.sparsity())
def __init__(self,objective,*args):
"""
optisolve(objective)
optisolve(objective,constraints)
"""
if len(args)>=1:
constraints = args[0]
else:
constraints = []
options = dict()
if len(args)>=2:
options = args[1]
if not isinstance(constraints,list):
raise Exception("Constraints must be given as list: [x>=0,y<=0]")
[ gl_pure, gl_equality] = sort_constraints( constraints )
symbols = OptimizationObject.get_primitives([objective]+gl_pure)
# helper functions for 'x'
X = C.veccat(*symbols["x"])
helper = C.Function('helper',[X],symbols["x"])
helper_inv = C.Function('helper_inv',symbols["x"],[X])
# helper functions for 'p' if applicable
if 'p' in symbols:
P = C.veccat(*symbols["p"])
self.Phelper_inv = C.Function('Phelper_inv',symbols["p"],[P])
else:
P = C.MX.sym('p',0,1)
if len(gl_pure)>0:
g_helpers = [];
for p in gl_pure:
g_helpers.append(C.MX.sym('g',p.sparsity()))
G_helpers = C.veccat(*g_helpers)
self.Ghelper = C.Function('Ghelper',[G_helpers],g_helpers)
self.Ghelper_inv = C.Function('Ghelper_inv',g_helpers,[G_helpers])
codegen = False;
if 'codegen' in options:
codegen = options["codegen"]
del options["codegen"]
opt = {}
if codegen:
options["jit"] = True
opt["jit"] = True
gl_pure_v = C.MX()
if len(gl_pure)>0:
gl_pure_v = C.veccat(*gl_pure)
if objective.is_vector() and objective.numel()>1:
F = C.vec(objective)
objective = 0.5*C.dot(F,F)
FF = C.Function('nlp',[X,P], [F])
JF = FF.jacobian()
J_out = JF.call([X,P])
J = J_out[0].T;
H = C.mtimes(J,J.T)
sigma = C.MX.sym('sigma')
Hf = C.Function('H',dict(x=X,p=P,lam_f=sigma,hess_gamma_x_x=sigma*C.triu(H)),['x', 'p', 'lam_f', 'lam_g'],['hess_gamma_x_x'],opt)
if "expand" in options and options["expand"]:
Hf = Hf.expand()
options["hess_lag"] = Hf
nlp = {"x":X,"p":P,"f":objective,"g":gl_pure_v}
self.solver = C.nlpsol('solver','ipopt', nlp, options)
# Save to class properties
self.symbols = symbols
self.helper = helper
self.helper_inv = helper_inv
self.gl_equality = gl_equality
self.resolve()
