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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 parametric NLPs in CasADi
* Note that there is currently no support for parametric sensitivities via this feature (although it would make a lot of sense).
* For parametric sensitivities, see the parametric_sensitivities.cpp example which calculates sensitivitied via the sIPOPT extension
* to IPOPT.
*
* Joel Andersson, KU Leuven 2012
*/
int main(){
/** Test problem (Ganesh & Biegler, A reduced Hessian strategy for sensitivity analysis of optimal flowsheets, AIChE 33, 1987, pp. 282-296)
*
* min x1^2 + x2^2 + x3^2
* s.t. 6*x1 + 3&x2 + 2*x3 - pi = 0
* p2*x1 + x2 - x3 - 1 = 0
* x1, x2, x3 >= 0
*
*/
// Optimization variables
SX x = SX::sym("x", 3);
// Parameters
SX p = SX::sym("p", 2);
// Objective
SX f = x(0)*x(0) + x(1)*x(1) + x(2)*x(2);
// Constraints
SX g = vertcat(
6*x(0) + 3*x(1) + 2*x(2) - p(0),
p(1)*x(0) + x(1) - x(2) - 1
);
// Initial guess and bounds for the optimization variables
std::vector<double> x0 = {0.15, 0.15, 0.00};
std::vector<double> lbx = {0.00, 0.00, 0.00};
std::vector<double> ubx = { inf, inf, inf};
// Nonlinear bounds
std::vector<double> lbg = {0.00, 0.00};
std::vector<double> ubg = {0.00, 0.00};
// Original parameter values
std::vector<double> p0 = {5.00, 1.00};
// NLP
SXDict nlp = {{"x", x}, {"p", p}, {"f", f}, {"g", g}};
// Create NLP solver and buffers
Function solver = nlpsol("solver", "ipopt", nlp);
std::map<std::string, DM> arg, res;
// Solve the NLP
arg["lbx"] = lbx;
arg["ubx"] = ubx;
arg["lbg"] = lbg;
arg["ubg"] = ubg;
arg["x0"] = x0;
arg["p"] = p0;
res = solver(arg);
// Print the solution
std::cout << "-----" << std::endl;
std::cout << "Optimal solution for p = " << arg.at("p") << ":" << std::endl;
std::cout << std::setw(30) << "Objective: " << res.at("f") << std::endl;
std::cout << std::setw(30) << "Primal solution: " << res.at("x") << std::endl;
std::cout << std::setw(30) << "Dual solution (x): " << res.at("lam_x") << std::endl;
std::cout << std::setw(30) << "Dual solution (g): " << res.at("lam_g") << std::endl;
// Change the parameter and resolve
arg["p"] = 4.5;
res = solver(arg);
// Print the new solution
std::cout << "-----" << std::endl;
std::cout << "Optimal solution for p = " << arg.at("p") << ":" << std::endl;
std::cout << std::setw(30) << "Objective: " << res.at("f") << std::endl;
std::cout << std::setw(30) << "Primal solution: " << res.at("x") << std::endl;
std::cout << std::setw(30) << "Dual solution (x): " << res.at("lam_x") << std::endl;
std::cout << std::setw(30) << "Dual solution (g): " << res.at("lam_g") << std::endl;
return 0;
}
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