jgillis

import casadi
from casadi import *
from casadi.casadi import OPTI_INEQUALITY, Opti_bounded
import matplotlib.pyplot as plt


# https://epubs.siam.org/doi/pdf/10.1137/16M1062569

m1 = 1
m2 = 0.3

jgillis

class Node:
  def __init__(self,val):
    self.val = val
    self.nodes = []

class AutoBrancher:
  OPEN = 0
  DONE = 1
  def __init__(self):

jgillis

Find and replace in casadi file:

casadi.Function('f',{x},{x},struct('dump_dir','matching_string')).save('test.casadi')
casadi.Function('f',{x},{x},struct('dump_dir','replace__string')).save('test2.casadi')

Check the difference in test and test2 to see the replacement.
Preservation of length only.

jgillis

from casadi import *


x = MX.sym('x')
y = MX.sym('y')
p = MX.sym('p')

nlp = {"x":vertcat(x,y),"p":p,"f":(x-2)**4+(y-3)**2}

solver = nlpsol('solver','ipopt',nlp)

jgillis

#
#     This file is part of rockit.
#
#     rockit -- Rapid Optimal Control Kit
#     Copyright (C) 2019 MECO, KU Leuven. All rights reserved.
#
#     Rockit is free software; you can redistribute it and/or
#     modify it under the terms of the GNU Lesser General Public
#     License as published by the Free Software Foundation; either
#     version 3 of the License, or (at your option) any later version.

jgillis

import casadi.*

x = MX.sym('x');
y = MX.sym('y');

xy = [x;y];
p = MX.sym('p');

nlp = struct;
nlp.x = xy;

jgillis

from casadi import SX, vertcat, inf

# Declare variables
x = SX.sym("x")
y = SX.sym("y")
z = SX.sym("z")

# Formulate the NLP
f = x**2 + 100*z**2
g = z + (1-x)**2 - y

jgillis

import numpy as np
from ctypes import *

class CasadiSparsity:
  """
  Compressed Columun storage sparsity pattern
  """
  def __init__(self,sp):
    self.n_row = sp[0]
    self.n_col = sp[1]

jgillis

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()

jgillis

import casadi.*

opti = Opti();

x = opti.variable(2);

fx = sin(x)-[2;0.3];

opti.minimize(sumsqr(fx));