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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 <casadi/casadi.hpp>
using namespace casadi;
/**
Solve the Rosenbrock problem, formulated as the NLP:
minimize x^2 + 100*z^2
subject to z+(1-x)^2-y == 0
Joel Andersson, 2015-2016
*/
int main(){
// Declare variables
SX x = SX::sym("x");
SX y = SX::sym("y");
SX z = SX::sym("z");
// Formulate the NLP
SX f = pow(x,2) + 100*pow(z,2);
SX g = z + pow(1-x, 2) - y;
SXDict nlp = {{"x", SX::vertcat({x,y,z})},
{"f", f},
{"g", g}};
// Create an NLP solver
Function solver = nlpsol("solver", "ipopt", nlp);
// Solve the Rosenbrock problem
DMDict arg;
arg["x0"] = std::vector<double>{2.5,3.0,0.75};
arg["lbg"] = arg["ubg"] = 0;
DMDict res = solver(arg);
// Print solution
std::cout << "Optimal cost: " << double(res.at("f")) << std::endl;
std::cout << "Primal solution: " << std::vector<double>(res.at("x")) << std::endl;
std::cout << "Dual solution (simple bounds): " << std::vector<double>(res.at("lam_x")) << std::endl;
std::cout << "Dual solution (nonlinear bounds): " << std::vector<double>(res.at("lam_g")) << std::endl;
return 0;
}
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