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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.
*
*/
#include <iostream>
#include <fstream>
#include <ctime>
#include <iomanip>
#include <casadi/casadi.hpp>
using namespace casadi;
/**
* Example program demonstrating NLP solution with Ipopt with callback functions as generated code
* Joel Andersson, 2013-2016
*/
int main(){
/** Test problem
*
* min x0^2 + x1^2
* s.t. x0 + x1 - 10 = 0
*/
// Optimization variables
MX x = MX::sym("x", 2);
// Objective
MX f = x(0)*x(0) + x(1)*x(1);
// Constraints
MX g = x(0)+x(1)-10;
// Create an NLP solver instance
Function solver = nlpsol("solver", "ipopt", {{"x", x}, {"f", f}, {"g", g}});
// Generate C code for the NLP functions
solver.generate_dependencies("nlp.c");
// Just-in-time compilation?
bool jit = false;
if (jit) {
// Create a new NLP solver instance using just-in-time compilation
solver = nlpsol("solver", "ipopt", "nlp.c");
} else {
// Compile the c-code
int flag = system("gcc -fPIC -shared -O3 nlp.c -o nlp.so");
casadi_assert(flag==0, "Compilation failed");
// Create a new NLP solver instance from the compiled code
solver = nlpsol("solver", "ipopt", "nlp.so");
}
// Bounds and initial guess
std::map<std::string, DM> arg, res;
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
std::cout << "-----" << std::endl;
std::cout << "objective at solution = " << res.at("f") << std::endl;
std::cout << "primal solution = " << res.at("x") << std::endl;
std::cout << "dual solution (x) = " << res.at("lam_x") << std::endl;
std::cout << "dual solution (g) = " << res.at("lam_g") << std::endl;
return 0;
}
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