Optimo  0.1.0
A C++ header library for optimization
 All Classes Functions Variables Pages
optimo::ProblemLS< Scalar > Class Template Reference

Class defining a convex Problem. More...

#include <objects_ls.h>

Public Member Functions

 ProblemLS (const ObjectiveFunctorLS< Scalar > &obj, const ConstraintsLS< Scalar > &con, const Matrix< Scalar, Dynamic, Dynamic > &Aeq, const Matrix< Scalar, Dynamic, 1 > &beq, const GradientFunctorLS< Scalar > &g, const HessianFunctorLS< Scalar > &h)
 Constrained Problem.
 
 ProblemLS (const ObjectiveFunctorLS< Scalar > &obj, const GradientFunctorLS< Scalar > &g, const HessianFunctorLS< Scalar > &h)
 Unconstrained problem.
 

Public Attributes

const ObjectiveFunctorLS
< Scalar > & 
objective
 Objective functor.
 
const ConstraintsLS< Scalar > & constraints
 Constraints.
 
const GradientFunctorLS< Scalar > & gradient
 Gradient functor.
 
const HessianFunctorLS< Scalar > & hessian
 Hessian functor.
 
const Matrix< Scalar, Dynamic,
Dynamic > 
A
 
const Matrix< Scalar, Dynamic, 1 > b
 Equality vector.
 

Detailed Description

template<typename Scalar>
class optimo::ProblemLS< Scalar >

Class defining a convex Problem.

The convex problem is assumed to have the following form:

\begin{eqnarray*} \underset{\mathbf{x}}{\text{minimize}} & f_0(\mathbf{x}) & \\ \text{subject to} & & \\ f_i(\mathbf{x}) & \leq & 0 \\ A_{\text{eq}} \mathbf{x} & = & \mathbf{b}_{\text{eq}} \end{eqnarray*}

This class Eigen matrices on the stack. Thus, use this class if the problem fits in the stack.


The documentation for this class was generated from the following file: