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#
# MIT No Attribution
#
# Copyright (C) 2010-2023 Joel Andersson, Joris Gillis, Moritz Diehl, KU Leuven.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this
# software and associated documentation files (the "Software"), to deal in the Software
# without restriction, including without limitation the rights to use, copy, modify,
# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
from casadi import *
# Test problem
#
# min x^2 + y^2
# s.t. x + y - 10 = 0
#
# Optimization variables
x = MX.sym("x")
y = MX.sym("y")
# Objective
f = x*x + y*y
# Constraints
g = x+y-10
# Create an NLP problem structure
nlp = {"x": vertcat(x,y), "f": f, "g": g}
mode = "jit"
# Pick a compiler
compiler = "gcc" # Linux
# compiler = "clang" # OSX
# compiler = "cl.exe" # Windows
# Run this script in an environment that recognised the compiler as command.
# On Windows, the suggested way is to run this script from a "x64 Native Tools Command Promt for VS" (Requires Visual C++ components or Build Tools for Visual Studio, available from Visual Studio installer. You also need SDK libraries in order to access stdio and math.)
flags = ["-O3"] # Linux/OSX
for mode in ["jit","external"]:
if mode=="jit":
# By default, the compiler will be gcc or cl.exe
jit_options = {"flags": flags, "verbose": True, "compiler": compiler}
options = {"jit": True, "compiler": "shell", "jit_options": jit_options}
# Create an NLP solver instance
solver = nlpsol("solver", "ipopt", nlp, options)
elif mode=="external":
# Create an NLP solver instance
solver = nlpsol("solver", "ipopt", nlp)
# Generate C code for the NLP functions
solver.generate_dependencies("nlp.c")
import subprocess
# On Windows, use other flags
cmd_args = [compiler,"-fPIC","-shared"]+flags+["nlp.c","-o","nlp.so"]
subprocess.run(cmd_args)
# Create a new NLP solver instance from the compiled code
solver = nlpsol("solver", "ipopt", "./nlp.so")
arg = {}
arg["lbx"] = -DM.inf()
arg["ubx"] = DM.inf()
arg["lbg"] = 0
arg["ubg"] = 0
arg["x0"] = 0
# Solve the NLP
res = solver(**arg)
# Print solution
print("-----")
print("objective at solution =", res["f"])
print("primal solution =", res["x"])
print("dual solution (x) =", res["lam_x"])
print("dual solution (g) =", res["lam_g"])
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