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#
# This file is part of CasADi.
#
# CasADi -- A symbolic framework for dynamic optimization.
# Copyright (C) 2010-2023 Joel Andersson, Joris Gillis, Moritz Diehl,
# KU Leuven. All rights reserved.
# Copyright (C) 2011-2014 Greg Horn
#
# CasADi 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.
#
# CasADi is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with CasADi; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#
# Callback
# =====================
from casadi import *
from numpy import *
# In this example, we will demonstrate callback functionality for Ipopt.
# Note that you need the fix https://github.com/casadi/casadi/wiki/enableIpoptCallback before this works
#
# We start with constructing the rosenbrock problem
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])
import matplotlib
if "Agg" not in matplotlib.get_backend():
matplotlib.interactive(True)
from pylab import figure, subplot, contourf, colorbar, draw, show, plot, title
import time
class MyCallback(Callback):
def __init__(self, name, nx, ng, np, opts={}):
Callback.__init__(self)
self.nx = nx
self.ng = ng
self.np = np
figure(1)
x_,y_ = mgrid[-1:1.5:0.01,-1:1.5:0.01]
z_ = DM.zeros(x_.shape)
for i in range(x_.shape[0]):
for j in range(x_.shape[1]):
z_[i,j] = fcn(x_[i,j],y_[i,j])
contourf(x_,y_,z_)
colorbar()
title('Iterations of Rosenbrock')
draw()
self.x_sols = []
self.y_sols = []
# Initialize internal objects
self.construct(name, opts)
def get_n_in(self): return nlpsol_n_out()
def get_n_out(self): return 1
def get_name_in(self, i): return nlpsol_out(i)
def get_name_out(self, i): return "ret"
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):
# Create dictionary
darg = {}
for (i,s) in enumerate(nlpsol_out()): darg[s] = arg[i]
sol = darg['x']
self.x_sols.append(float(sol[0]))
self.y_sols.append(float(sol[1]))
if hasattr(self,'lines'):
if "template" not in matplotlib.get_backend(): # Broken for template: https://github.com/matplotlib/matplotlib/issues/8516/
self.lines[0].set_data(self.x_sols,self.y_sols)
else:
self.lines = plot(self.x_sols,self.y_sols,'or-')
draw()
time.sleep(0.25)
return [0]
mycallback = MyCallback('mycallback', 2, 1, 0)
opts = {}
opts['iteration_callback'] = mycallback
opts['ipopt.tol'] = 1e-8
opts['ipopt.max_iter'] = 50
solver = nlpsol('solver', 'ipopt', nlp, opts)
sol = solver(lbx=-10, ubx=10, lbg=-10, ubg=10)
# By setting matplotlib interactivity off, we can inspect the figure at ease
matplotlib.interactive(False)
show()
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