Dear forum members,
I am trying to solve a simple non-linear optimization problem problem that use IPOPT solver. I am trying to use C++ interface and IPOPT source code with visual studio 2017. I downloaded this version Ipopt-3.12.12. from
https://www.coin-or.org/download/source/Ipopt/
The source code to develop model and call IPOPT is showed below
How can I integrate all source codes (e.g. ipopt source code, external code that does not come with ipopt source code etc.) in visual studio 2017 to solve the model?
It would be very helpful if any of the group member would share implementation experience of C++ interface of IPOPT in visual studio 2013/2015/2017.
Thanks in advance
Best,
Roni
// Copyright (C) 2005, 2009 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// $Id: hs071_main.cpp 2398 2013-10-19 18:08:59Z stefan $
//
// Authors: Carl Laird, Andreas Waechter IBM 2005-08-10
#include "IpIpoptApplication.hpp"
#include "hs071_nlp.hpp"
#include <iostream>
using namespace Ipopt;
int main(int argv, char* argc[])
{
// Create a new instance of your nlp
// (use a SmartPtr, not raw)
SmartPtr<TNLP> mynlp = new HS071_NLP();
// Create a new instance of IpoptApplication
// (use a SmartPtr, not raw)
// We are using the factory, since this allows us to compile this
// example with an Ipopt Windows DLL
SmartPtr<IpoptApplication> app = IpoptApplicationFactory();
app->RethrowNonIpoptException(true);
// Change some options
// Note: The following choices are only examples, they might not be
// suitable for your optimization problem.
app->Options()->SetNumericValue("tol", 1e-7);
app->Options()->SetStringValue("mu_strategy", "adaptive");
app->Options()->SetStringValue("output_file", "ipopt.out");
// The following overwrites the default name (ipopt.opt) of the
// options file
// app->Options()->SetStringValue("option_file_name", "hs071.opt");
// Initialize the IpoptApplication and process the options
ApplicationReturnStatus status;
status = app->Initialize();
if (status != Solve_Succeeded) {
std::cout << std::endl << std::endl << "*** Error during initialization!" << std::endl;
return (int) status;
}
// Ask Ipopt to solve the problem
status = app->OptimizeTNLP(mynlp);
if (status == Solve_Succeeded) {
std::cout << std::endl << std::endl << "*** The problem solved!" << std::endl;
}
else {
std::cout << std::endl << std::endl << "*** The problem FAILED!" << std::endl;
}
// As the SmartPtrs go out of scope, the reference count
// will be decremented and the objects will automatically
// be deleted.
return (int) status;
}// Copyright (C) 2005, 2006 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// $Id: hs071_nlp.cpp 2005 2011-06-06 12:55:16Z stefan $
//
// Authors: Carl Laird, Andreas Waechter IBM 2005-08-16
#include "hs071_nlp.hpp"
#include <cassert>
#include <iostream>
using namespace Ipopt;
// constructor
HS071_NLP::HS071_NLP()
{}
//destructor
HS071_NLP::~HS071_NLP()
{}
// returns the size of the problem
bool HS071_NLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
Index& nnz_h_lag, IndexStyleEnum& index_style)
{
// The problem described in HS071_NLP.hpp has 4 variables, x[0] through x[3]
n = 4;
// one equality constraint and one inequality constraint
m = 2;
// in this example the jacobian is dense and contains 8 nonzeros
nnz_jac_g = 8;
// the hessian is also dense and has 16 total nonzeros, but we
// only need the lower left corner (since it is symmetric)
nnz_h_lag = 10;
// use the C style indexing (0-based)
index_style = TNLP::C_STYLE;
return true;
}
// returns the variable bounds
bool HS071_NLP::get_bounds_info(Index n, Number* x_l, Number* x_u,
Index m, Number* g_l, Number* g_u)
{
// here, the n and m we gave IPOPT in get_nlp_info are passed back to us.
// If desired, we could assert to make sure they are what we think they are.
assert(n == 4);
assert(m == 2);
// the variables have lower bounds of 1
for (Index i=0; i<4; i++) {
x_l[i] = 1.0;
}
// the variables have upper bounds of 5
for (Index i=0; i<4; i++) {
x_u[i] = 5.0;
}
// the first constraint g1 has a lower bound of 25
g_l[0] = 25;
// the first constraint g1 has NO upper bound, here we set it to 2e19.
// Ipopt interprets any number greater than nlp_upper_bound_inf as
// infinity. The default value of nlp_upper_bound_inf and nlp_lower_bound_inf
// is 1e19 and can be changed through ipopt options.
g_u[0] = 2e19;
// the second constraint g2 is an equality constraint, so we set the
// upper and lower bound to the same value
g_l[1] = g_u[1] = 40.0;
return true;
}
// returns the initial point for the problem
bool HS071_NLP::get_starting_point(Index n, bool init_x, Number* x,
bool init_z, Number* z_L, Number* z_U,
Index m, bool init_lambda,
Number* lambda)
{
// Here, we assume we only have starting values for x, if you code
// your own NLP, you can provide starting values for the dual variables
// if you wish
assert(init_x == true);
assert(init_z == false);
assert(init_lambda == false);
// initialize to the given starting point
x[0] = 1.0;
x[1] = 5.0;
x[2] = 5.0;
x[3] = 1.0;
return true;
}
// returns the value of the objective function
bool HS071_NLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)
{
assert(n == 4);
obj_value = x[0] * x[3] * (x[0] + x[1] + x[2]) + x[2];
return true;
}
// return the gradient of the objective function grad_{x} f(x)
bool HS071_NLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)
{
assert(n == 4);
grad_f[0] = x[0] * x[3] + x[3] * (x[0] + x[1] + x[2]);
grad_f[1] = x[0] * x[3];
grad_f[2] = x[0] * x[3] + 1;
grad_f[3] = x[0] * (x[0] + x[1] + x[2]);
return true;
}
// return the value of the constraints: g(x)
bool HS071_NLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)
{
assert(n == 4);
assert(m == 2);
g[0] = x[0] * x[1] * x[2] * x[3];
g[1] = x[0]*x[0] + x[1]*x[1] + x[2]*x[2] + x[3]*x[3];
return true;
}
// return the structure or values of the jacobian
bool HS071_NLP::eval_jac_g(Index n, const Number* x, bool new_x,
Index m, Index nele_jac, Index* iRow, Index *jCol,
Number* values)
{
if (values == NULL) {
// return the structure of the jacobian
// this particular jacobian is dense
iRow[0] = 0;
jCol[0] = 0;
iRow[1] = 0;
jCol[1] = 1;
iRow[2] = 0;
jCol[2] = 2;
iRow[3] = 0;
jCol[3] = 3;
iRow[4] = 1;
jCol[4] = 0;
iRow[5] = 1;
jCol[5] = 1;
iRow[6] = 1;
jCol[6] = 2;
iRow[7] = 1;
jCol[7] = 3;
}
else {
// return the values of the jacobian of the constraints
values[0] = x[1]*x[2]*x[3]; // 0,0
values[1] = x[0]*x[2]*x[3]; // 0,1
values[2] = x[0]*x[1]*x[3]; // 0,2
values[3] = x[0]*x[1]*x[2]; // 0,3
values[4] = 2*x[0]; // 1,0
values[5] = 2*x[1]; // 1,1
values[6] = 2*x[2]; // 1,2
values[7] = 2*x[3]; // 1,3
}
return true;
}
//return the structure or values of the hessian
bool HS071_NLP::eval_h(Index n, const Number* x, bool new_x,
Number obj_factor, Index m, const Number* lambda,
bool new_lambda, Index nele_hess, Index* iRow,
Index* jCol, Number* values)
{
if (values == NULL) {
// return the structure. This is a symmetric matrix, fill the lower left
// triangle only.
// the hessian for this problem is actually dense
Index idx=0;
for (Index row = 0; row < 4; row++) {
for (Index col = 0; col <= row; col++) {
iRow[idx] = row;
jCol[idx] = col;
idx++;
}
}
assert(idx == nele_hess);
}
else {
// return the values. This is a symmetric matrix, fill the lower left
// triangle only
// fill the objective portion
values[0] = obj_factor * (2*x[3]); // 0,0
values[1] = obj_factor * (x[3]); // 1,0
values[2] = 0.; // 1,1
values[3] = obj_factor * (x[3]); // 2,0
values[4] = 0.; // 2,1
values[5] = 0.; // 2,2
values[6] = obj_factor * (2*x[0] + x[1] + x[2]); // 3,0
values[7] = obj_factor * (x[0]); // 3,1
values[8] = obj_factor * (x[0]); // 3,2
values[9] = 0.; // 3,3
// add the portion for the first constraint
values[1] += lambda[0] * (x[2] * x[3]); // 1,0
values[3] += lambda[0] * (x[1] * x[3]); // 2,0
values[4] += lambda[0] * (x[0] * x[3]); // 2,1
values[6] += lambda[0] * (x[1] * x[2]); // 3,0
values[7] += lambda[0] * (x[0] * x[2]); // 3,1
values[8] += lambda[0] * (x[0] * x[1]); // 3,2
// add the portion for the second constraint
values[0] += lambda[1] * 2; // 0,0
values[2] += lambda[1] * 2; // 1,1
values[5] += lambda[1] * 2; // 2,2
values[9] += lambda[1] * 2; // 3,3
}
return true;
}
void HS071_NLP::finalize_solution(SolverReturn status,
Index n, const Number* x, const Number* z_L, const Number* z_U,
Index m, const Number* g, const Number* lambda,
Number obj_value,
const IpoptData* ip_data,
IpoptCalculatedQuantities* ip_cq)
{
// here is where we would store the solution to variables, or write to a file, etc
// so we could use the solution.
// For this example, we write the solution to the console
std::cout << std::endl << std::endl << "Solution of the primal variables, x" << std::endl;
for (Index i=0; i<n; i++) {
std::cout << "x[" << i << "] = " << x[i] << std::endl;
}
std::cout << std::endl << std::endl << "Solution of the bound multipliers, z_L and z_U" << std::endl;
for (Index i=0; i<n; i++) {
std::cout << "z_L[" << i << "] = " << z_L[i] << std::endl;
}
for (Index i=0; i<n; i++) {
std::cout << "z_U[" << i << "] = " << z_U[i] << std::endl;
}
std::cout << std::endl << std::endl << "Objective value" << std::endl;
std::cout << "f(x*) = " << obj_value << std::endl;
std::cout << std::endl << "Final value of the constraints:" << std::endl;
for (Index i=0; i<m ;i++) {
std::cout << "g(" << i << ") = " << g[i] << std::endl;
}
}// Copyright (C) 2005, 2007 International Business Machines and others.
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// $Id: hs071_nlp.hpp 1861 2010-12-21 21:34:47Z andreasw $
//
// Authors: Carl Laird, Andreas Waechter IBM 2005-08-09
#ifndef __HS071_NLP_HPP__
#define __HS071_NLP_HPP__
#include "IpTNLP.hpp"
using namespace Ipopt;
/** C++ Example NLP for interfacing a problem with IPOPT.
* HS071_NLP implements a C++ example of problem 71 of the
* Hock-Schittkowski test suite. This example is designed to go
* along with the tutorial document and show how to interface
* with IPOPT through the TNLP interface.
*
* Problem hs071 looks like this
*
* min x1*x4*(x1 + x2 + x3) + x3
* s.t. x1*x2*x3*x4 >= 25
* x1**2 + x2**2 + x3**2 + x4**2 = 40
* 1 <= x1,x2,x3,x4 <= 5
*
* Starting point:
* x = (1, 5, 5, 1)
*
* Optimal solution:
* x = (1.00000000, 4.74299963, 3.82114998, 1.37940829)
*
*
*/
class HS071_NLP : public TNLP
{
public:
/** default constructor */
HS071_NLP();
/** default destructor */
virtual ~HS071_NLP();
/**@name Overloaded from TNLP */
//@{
/** Method to return some info about the nlp */
virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
Index& nnz_h_lag, IndexStyleEnum& index_style);
/** Method to return the bounds for my problem */
virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u,
Index m, Number* g_l, Number* g_u);
/** Method to return the starting point for the algorithm */
virtual bool get_starting_point(Index n, bool init_x, Number* x,
bool init_z, Number* z_L, Number* z_U,
Index m, bool init_lambda,
Number* lambda);
/** Method to return the objective value */
virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value);
/** Method to return the gradient of the objective */
virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f);
/** Method to return the constraint residuals */
virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g);
/** Method to return:
* 1) The structure of the jacobian (if "values" is NULL)
* 2) The values of the jacobian (if "values" is not NULL)
*/
virtual bool eval_jac_g(Index n, const Number* x, bool new_x,
Index m, Index nele_jac, Index* iRow, Index *jCol,
Number* values);
/** Method to return:
* 1) The structure of the hessian of the lagrangian (if "values" is NULL)
* 2) The values of the hessian of the lagrangian (if "values" is not NULL)
*/
virtual bool eval_h(Index n, const Number* x, bool new_x,
Number obj_factor, Index m, const Number* lambda,
bool new_lambda, Index nele_hess, Index* iRow,
Index* jCol, Number* values);
//@}
/** @name Solution Methods */
//@{
/** This method is called when the algorithm is complete so the TNLP can store/write the solution */
virtual void finalize_solution(SolverReturn status,
Index n, const Number* x, const Number* z_L, const Number* z_U,
Index m, const Number* g, const Number* lambda,
Number obj_value,
const IpoptData* ip_data,
IpoptCalculatedQuantities* ip_cq);
//@}
private:
/**@name Methods to block default compiler methods.
* The compiler automatically generates the following three methods.
* Since the default compiler implementation is generally not what
* you want (for all but the most simple classes), we usually
* put the declarations of these methods in the private section
* and never implement them. This prevents the compiler from
* implementing an incorrect "default" behavior without us
* knowing. (See Scott Meyers book, "Effective C++")
*
*/
//@{
// HS071_NLP();
HS071_NLP(const HS071_NLP&);
HS071_NLP& operator=(const HS071_NLP&);
//@}
};
#endif