Detailed Description

Conversion utilities for optimal control problems.

For example: converting an OCP into a sparse “multiple shooting” quadratic program, or computing the gradient of the quadratic OCP cost function.

Definition in file conversion.hpp.

#include <cyqlone/config.hpp>
#include <cyqlone/ocp.hpp>
#include <guanaqo/linalg/sparsity.hpp>
#include <vector>

Classes
struct	cyqlone::LinearOCPSparseQP
	Represents a sparse multiple shooting formulation of the standard optimal control problem. More...
struct	cyqlone::LinearOCPSparseQP::KKTMatrix
struct	cyqlone::SparseCSC< class Index, class StorageIndex >

Namespaces
namespace	cyqlone

Functions
void	cyqlone::reference_to_gradient (const LinearOCPStorage &ocp, std::span< const real_t > ref, std::span< real_t > qr)
	Simply computes the gradient of the quadratic cost \( J(x, u) = \sum_{j=1}^{N-1} \ell_j(x^j, u^j) + \ell_N(x^N) \), with \( \ell_j(x, u) = \tfrac12 \left\\| \begin{pmatrix} x - x^j_\text{ref} \\ u - u^j_\text{ref} \end{pmatrix} \right\\|_{H_j}^2 \), with the Hessian \( H_j = \begin{pmatrix} Q_j & S_j^\top \\ S_j & R_j \end{pmatrix} \).
void	cyqlone::reference_to_gradient (LinearOCPStorage &ocp, std::span< const real_t > ref)
	Simply computes the gradient of the quadratic cost \( J(x, u) = \sum_{j=1}^{N-1} \ell_j(x^j, u^j) + \ell_N(x^N) \), with \( \ell_j(x, u) = \tfrac12 \left\\| \begin{pmatrix} x - x^j_\text{ref} \\ u - u^j_\text{ref} \end{pmatrix} \right\\|_{H_j}^2 \), with the Hessian \( H_j = \begin{pmatrix} Q_j & S_j^\top \\ S_j & R_j \end{pmatrix} \).

Class Documentation

struct cyqlone::LinearOCPSparseQP::KKTMatrix