ipopt_adapter.cc
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26 
27 #include <ifopt/ipopt_adapter.h>
28 
29 namespace Ipopt {
30 
31 IpoptAdapter::IpoptAdapter(Problem& nlp, bool finite_diff)
32 {
33  nlp_ = &nlp;
34  finite_diff_ = finite_diff;
35 }
36 
37 bool IpoptAdapter::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
38  Index& nnz_h_lag, IndexStyleEnum& index_style)
39 {
42 
43  if (finite_diff_)
44  nnz_jac_g = m*n;
45  else
46  nnz_jac_g = nlp_->GetJacobianOfConstraints().nonZeros();
47 
48  nnz_h_lag = n*n;
49 
50  // start index at 0 for row/col entries
51  index_style = C_STYLE;
52 
53  return true;
54 }
55 
56 bool IpoptAdapter::get_bounds_info(Index n, double* x_lower, double* x_upper,
57  Index m, double* g_l, double* g_u)
58 {
59  auto bounds_x = nlp_->GetBoundsOnOptimizationVariables();
60  for (std::size_t c=0; c<bounds_x.size(); ++c) {
61  x_lower[c] = bounds_x.at(c).lower_;
62  x_upper[c] = bounds_x.at(c).upper_;
63  }
64 
65  // specific bounds depending on equality and inequality constraints
66  auto bounds_g = nlp_->GetBoundsOnConstraints();
67  for (std::size_t c=0; c<bounds_g.size(); ++c) {
68  g_l[c] = bounds_g.at(c).lower_;
69  g_u[c] = bounds_g.at(c).upper_;
70  }
71 
72  return true;
73 }
74 
75 bool IpoptAdapter::get_starting_point(Index n, bool init_x, double* x,
76  bool init_z, double* z_L, double* z_U,
77  Index m, bool init_lambda,
78  double* lambda)
79 {
80  // Here, we assume we only have starting values for x
81  assert(init_x == true);
82  assert(init_z == false);
83  assert(init_lambda == false);
84 
85  VectorXd x_all = nlp_->GetVariableValues();
86  Eigen::Map<VectorXd>(&x[0], x_all.rows()) = x_all;
87 
88  return true;
89 }
90 
91 bool IpoptAdapter::eval_f(Index n, const double* x, bool new_x, double& obj_value)
92 {
93  obj_value = nlp_->EvaluateCostFunction(x);
94  return true;
95 }
96 
97 bool IpoptAdapter::eval_grad_f(Index n, const double* x, bool new_x, double* grad_f)
98 {
99  Eigen::VectorXd grad = nlp_->EvaluateCostFunctionGradient(x, finite_diff_);
100  Eigen::Map<Eigen::MatrixXd>(grad_f,n,1) = grad;
101  return true;
102 }
103 
104 bool IpoptAdapter::eval_g(Index n, const double* x, bool new_x, Index m, double* g)
105 {
106  VectorXd g_eig = nlp_->EvaluateConstraints(x);
107  Eigen::Map<VectorXd>(g,m) = g_eig;
108  return true;
109 }
110 
111 bool IpoptAdapter::eval_jac_g(Index n, const double* x, bool new_x,
112  Index m, Index nele_jac, Index* iRow, Index *jCol,
113  double* values)
114 {
115  // defines the positions of the nonzero elements of the jacobian
116  if (values == NULL) {
117  // If "jacobian_approximation" option is set as "finite-difference-values", the Jacobian is dense!
118  Index nele=0;
119  if (finite_diff_) { // dense jacobian
120  for (Index row = 0; row < m; row++) {
121  for (Index col = 0; col < n; col++) {
122  iRow[nele] = row;
123  jCol[nele] = col;
124  nele++;
125  }
126  }
127  }
128  else { // sparse jacobian
129  auto jac = nlp_->GetJacobianOfConstraints();
130  for (int k=0; k<jac.outerSize(); ++k) {
131  for (Jacobian::InnerIterator it(jac,k); it; ++it) {
132  iRow[nele] = it.row();
133  jCol[nele] = it.col();
134  nele++;
135  }
136  }
137  }
138 
139  assert(nele == nele_jac); // initial sparsity structure is never allowed to change
140  }
141  else {
142  // only gets used if "jacobian_approximation finite-difference-values" is not set
143  nlp_->EvalNonzerosOfJacobian(x, values);
144  }
145 
146  return true;
147 }
148 
149 bool IpoptAdapter::intermediate_callback(AlgorithmMode mode,
150  Index iter, double obj_value,
151  double inf_pr, double inf_du,
152  double mu, double d_norm,
153  double regularization_size,
154  double alpha_du, double alpha_pr,
155  Index ls_trials,
156  const IpoptData* ip_data,
157  IpoptCalculatedQuantities* ip_cq)
158 {
159  nlp_->SaveCurrent();
160  return true;
161 }
162 
163 void IpoptAdapter::finalize_solution(SolverReturn status,
164  Index n, const double* x, const double* z_L, const double* z_U,
165  Index m, const double* g, const double* lambda,
166  double obj_value,
167  const IpoptData* ip_data,
168  IpoptCalculatedQuantities* ip_cq)
169 {
170  nlp_->SetVariables(x);
171  nlp_->SaveCurrent();
172 }
173 
174 } // namespace Ipopt
int GetNumberOfConstraints() const
The number of individual constraints.
Definition: problem.cc:133
A generic optimization problem with variables, costs and constraints.
Definition: problem.h:97
virtual bool eval_grad_f(Index n, const double *x, bool new_x, double *grad_f)
double EvaluateCostFunction(const double *x)
The scalar cost for current optimization variables x.
Definition: problem.cc:89
IpoptAdapter(Problem &nlp, bool finite_diff=false)
Creates an IpoptAdapter wrapping the nlp.
VectorXd EvaluateCostFunctionGradient(const double *x, bool use_finite_difference_approximation=false, double epsilon=std::numeric_limits< double >::epsilon())
The column-vector of derivatives of the cost w.r.t. each variable.
Definition: problem.cc:100
virtual bool get_bounds_info(Index n, double *x_l, double *x_u, Index m, double *g_l, double *g_u)
VectorXd GetVariableValues() const
The current value of the optimization variables.
Definition: problem.cc:77
virtual bool get_nlp_info(Index &n, Index &m, Index &nnz_jac_g, Index &nnz_h_lag, IndexStyleEnum &index_style)
namespace defined by the Ipopt solver.
Definition: ipopt_adapter.h:42
void SaveCurrent()
Saves the current values of the optimization variables in x_prev.
Definition: problem.cc:174
int GetNumberOfOptimizationVariables() const
The number of optimization variables.
Definition: problem.cc:65
void SetVariables(const double *x)
Updates the variables with the values of the raw pointer x.
Definition: problem.cc:83
Jacobian GetJacobianOfConstraints() const
The sparse-matrix representation of Jacobian of the constraints.
Definition: problem.cc:162
virtual bool eval_f(Index n, const double *x, bool new_x, double &obj_value)
VecBound GetBoundsOnConstraints() const
The upper and lower bound of each individual constraint.
Definition: problem.cc:127
virtual void finalize_solution(SolverReturn status, Index n, const double *x, const double *z_L, const double *z_U, Index m, const double *g, const double *lambda, double obj_value, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)
Problem * nlp_
The solver independent problem definition.
Definition: ipopt_adapter.h:70
virtual bool eval_jac_g(Index n, const double *x, bool new_x, Index m, Index nele_jac, Index *iRow, Index *jCol, double *values)
Problem::VectorXd VectorXd
Definition: ipopt_adapter.h:57
void EvalNonzerosOfJacobian(const double *x, double *values)
Extracts those entries from constraint Jacobian that are not zero.
Definition: problem.cc:152
virtual bool get_starting_point(Index n, bool init_x, double *x, bool init_z, double *z_L, double *z_U, Index m, bool init_lambda, double *lambda)
virtual bool eval_g(Index n, const double *x, bool new_x, Index m, double *g)
virtual bool intermediate_callback(AlgorithmMode mode, Index iter, double obj_value, double inf_pr, double inf_du, double mu, double d_norm, double regularization_size, double alpha_du, double alpha_pr, Index ls_trials, const IpoptData *ip_data, IpoptCalculatedQuantities *ip_cq)
VectorXd EvaluateConstraints(const double *x)
Each constraint value g(x) for current optimization variables x.
Definition: problem.cc:139
VecBound GetBoundsOnOptimizationVariables() const
The maximum and minimum value each optimization variable is allowed to have.
Definition: problem.cc:71
bool finite_diff_
Flag that indicates the "finite-difference-values" option is set.
Definition: ipopt_adapter.h:71


ifopt
Author(s): Alexander W. Winkler
autogenerated on Fri Sep 16 2022 02:15:26