smooth::feedback::NLP Concept Reference

Nonlinear Programming Problem. More...

#include <nlp.hpp>

Concept definition

template<typename T>
concept smooth::feedback::NLP =  requires(
  std::decay_t<T> & nlp, const Eigen::Ref<const Eigen::VectorXd> x, const Eigen::Ref<const Eigen::VectorXd> lambda)
{
  
  {nlp.n()} -> std::convertible_to<std::size_t>;
  {nlp.m()} -> std::convertible_to<std::size_t>;
 
  
  {nlp.xl()} -> std::convertible_to<Eigen::VectorXd>;
  {nlp.xu()} -> std::convertible_to<Eigen::VectorXd>;
 
  
  {nlp.f(x)}     -> std::convertible_to<double>;
  {nlp.df_dx(x)} -> std::convertible_to<Eigen::SparseMatrix<double>>;
 
  
  {nlp.g(x)}     -> std::convertible_to<Eigen::VectorXd>;
  {nlp.gl()}     -> std::convertible_to<Eigen::VectorXd>;
  {nlp.gu()}     -> std::convertible_to<Eigen::VectorXd>;
  {nlp.dg_dx(x)} -> std::convertible_to<Eigen::SparseMatrix<double>>;
  
}

Detailed Description

Nonlinear Programming Problem.

\[ \begin{cases} \min_{x} & f(x) \\ \text{s.t.} & x_l \leq x \leq x_u \\ & g_l \leq g(x) \leq g_u \end{cases} \]

for \( f : \mathbb{R}^n \rightarrow \mathbb{R} \) and \( g : \mathbb{R}^n \rightarrow \mathbb{R}^m \).

Definition at line 31 of file nlp.hpp.