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));

jgillis

import casadi.*

% Per https://en.wikipedia.org/wiki/Numerical_integration
% F(x) = int_a^x f(u) du
% <->   dF(x)/dx = f(x), F(a)=0


f = @(x) x.^5;
interv = [1.1 2.3];

jgillis

from casadi import *

opti = Opti()

N = 5
degree = 3

grid = np.linspace(0, 6, N)

# knots ~ grid but with multiplicity

jgillis

close all

% casadi.interpolant creates 'knots' and 'coefficients' out of data and passes it on to casadi.Function.bspline
% Here, we supply knots and coefficients directly to casadi.Function.bspline


%% Example 1: discontinuous example, just to make clear the interpretation
% of the pp = piecewise polynomial form clear

% 4 intervals: 0-1, 1-2, 2-4, 4-6