#include <sleipnir/optimization/solver/sqp.hpp>
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std::function< double(const Eigen::VectorXd &x)> | f |
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std::function< Eigen::SparseVector< double >(const Eigen::VectorXd &x)> | g |
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std::function< Eigen::SparseMatrix< double >(const Eigen::VectorXd &x, const Eigen::VectorXd &y)> | H |
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std::function< Eigen::VectorXd(const Eigen::VectorXd &x)> | c_e |
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std::function< Eigen::SparseMatrix< double >(const Eigen::VectorXd &x)> | A_e |
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Matrix callbacks for the Sequential Quadratic Programming (SQP) solver.
◆ A_e
std::function<Eigen::SparseMatrix<double>(const Eigen::VectorXd& x)> slp::SQPMatrixCallbacks::A_e |
Equality constraint Jacobian ∂cₑ/∂x getter.
/// [∇ᵀcₑ₁(xₖ)]
/// Aₑ(x) = [∇ᵀcₑ₂(xₖ)]
/// [ ⋮ ]
/// [∇ᵀcₑₘ(xₖ)]
///
<table>
<tr>
<th>Variable</th>
<th>Rows</th>
<th>Columns</th>
</tr>
<tr>
<td>x</td>
<td>num_decision_variables</td>
<td>1</td>
</tr>
<tr>
<td>Aₑ(x)</td>
<td>num_equality_constraints</td>
<td>num_decision_variables</td>
</tr>
</table>
◆ c_e
std::function<Eigen::VectorXd(const Eigen::VectorXd& x)> slp::SQPMatrixCallbacks::c_e |
Equality constraint value cₑ(x) getter.
Variable | Rows | Columns |
x | num_decision_variables | 1 |
cₑ(x) | num_equality_constraints | 1 |
std::function<double(const Eigen::VectorXd& x)> slp::SQPMatrixCallbacks::f |
Cost function value f(x) getter.
std::function<Eigen::SparseVector<double>(const Eigen::VectorXd& x)> slp::SQPMatrixCallbacks::g |
Cost function gradient ∇f(x) getter.
Variable | Rows | Columns |
x | num_decision_variables | 1 |
∇f(x) | num_decision_variables | 1 |
std::function<Eigen::SparseMatrix<double>(const Eigen::VectorXd& x, const Eigen::VectorXd& y)> slp::SQPMatrixCallbacks::H |
Lagrangian Hessian ∇ₓₓ²L(x, y) getter.
L(xₖ, yₖ) = f(xₖ) − yₖᵀcₑ(xₖ)
Variable | Rows | Columns |
x | num_decision_variables | 1 |
y | num_equality_constraints | 1 |
∇ₓₓ²L(x, y) | num_decision_variables | num_decision_variables |
The documentation for this struct was generated from the following file:
- include/sleipnir/optimization/solver/sqp.hpp