Custom stopping criterion

test.py

from casadi import *

x=SX.sym("x")
y=SX.sym("y")

f = (1-x)**2+100*(y-x**2)**2
nlp={'x':vertcat(x,y), 'f':f,'g':x+y}
fcn = Function('f', [x, y], [f])

callback_inputs = dict()
for i, label in enumerate(nlpsol_out()):
  callback_inputs[label] = i

class MyCallback(Callback):
  def __init__(self,nx, ng, np):
    Callback.__init__(self)

    self.nx = nx
    self.ng = ng
    self.np = np
    self.construct("mycallback", {})

  def get_n_in(self): return nlpsol_n_out()
  def get_n_out(self): return 1


  def get_sparsity_in(self, i):
    n = nlpsol_out(i)
    if n=='f':
      return Sparsity. scalar()
    elif n in ('x', 'lam_x'):
      return Sparsity.dense(self.nx)
    elif n in ('g', 'lam_g'):
      return Sparsity.dense(self.ng)
    else:
      return Sparsity(0,0)
  def eval(self, arg):
    objective = arg[callback_inputs["f"]]
    if objective<1e-4:
      # end optimization
      return [1]
    else:
      return [0]

mycallback = MyCallback(2,1,0)
opts = {}
opts['iteration_callback'] = mycallback
solver = nlpsol('solver', 'ipopt', nlp, opts)
sol = solver(lbx=-10, ubx=10, lbg=-10, ubg=10)