slp::Problem< Scalar > Class Template Reference

#include <sleipnir/optimization/problem.hpp>

Inheritance diagram for slp::Problem< Scalar >:

Public Member Functions
	Problem () noexcept=default
Variable< Scalar >	decision_variable ()
VariableMatrix< Scalar >	decision_variable (int rows, int cols=1)
VariableMatrix< Scalar >	symmetric_decision_variable (int rows)
void	minimize (const Variable< Scalar > &cost)
void	minimize (Variable< Scalar > &&cost)
void	maximize (const Variable< Scalar > &objective)
void	maximize (Variable< Scalar > &&objective)
void	subject_to (const EqualityConstraints< Scalar > &constraint)
void	subject_to (EqualityConstraints< Scalar > &&constraint)
void	subject_to (const InequalityConstraints< Scalar > &constraint)
void	subject_to (InequalityConstraints< Scalar > &&constraint)
ExpressionType	cost_function_type () const
ExpressionType	equality_constraint_type () const
ExpressionType	inequality_constraint_type () const
ExitStatus	solve (const Options &options=Options{}, bool spy=false)
template<typename F > requires requires(F callback, const IterationInfo<Scalar>& info) { { callback(info) } -> std::same_as<void>; }
void	add_callback (F &&callback)
template<typename F > requires requires(F callback, const IterationInfo<Scalar>& info) { { callback(info) } -> std::same_as<bool>; }
void	add_callback (F &&callback)
void	clear_callbacks ()
template<typename F > requires requires(F callback, const IterationInfo<Scalar>& info) { { callback(info) } -> std::same_as<bool>; }
void	add_persistent_callback (F &&callback)

Detailed Description

template<typename Scalar>
class slp::Problem< Scalar >

This class allows the user to pose a constrained nonlinear optimization problem in natural mathematical notation and solve it.

This class supports problems of the form:

      minₓ f(x)
subject to cₑ(x) = 0
           cᵢ(x) ≥ 0

where f(x) is the scalar cost function, x is the vector of decision variables (variables the solver can tweak to minimize the cost function), cᵢ(x) are the inequality constraints, and cₑ(x) are the equality constraints. Constraints are equations or inequalities of the decision variables that constrain what values the solver is allowed to use when searching for an optimal solution.

The nice thing about this class is users don't have to put their system in the form shown above manually; they can write it in natural mathematical form and it'll be converted for them.

Template Parameters

Scalar

Scalar type.

Constructor & Destructor Documentation

Problem()

template<typename Scalar >

slp::Problem< Scalar >::Problem ( )

defaultnoexcept

Construct the optimization problem.

Member Function Documentation

add_callback() [1/2]

template<typename Scalar >

template<typename F >
requires requires(F callback, const IterationInfo<Scalar>& info) { { callback(info) } -> std::same_as<void>; }

void slp::Problem< Scalar >::add_callback ( F &&  callback )

inline

Adds a callback to be called at the beginning of each solver iteration.

The callback for this overload should return void.

Parameters

callback

The callback.

add_callback() [2/2]

template<typename Scalar >

template<typename F >
requires requires(F callback, const IterationInfo<Scalar>& info) { { callback(info) } -> std::same_as<bool>; }

void slp::Problem< Scalar >::add_callback ( F &&  callback )

inline

Adds a callback to be called at the beginning of each solver iteration.

The callback for this overload should return bool.

Parameters

callback

The callback. Returning true from the callback causes the solver to exit early with the solution it has so far.

add_persistent_callback()

template<typename Scalar >

template<typename F >
requires requires(F callback, const IterationInfo<Scalar>& info) { { callback(info) } -> std::same_as<bool>; }

void slp::Problem< Scalar >::add_persistent_callback ( F &&  callback )

inline

Adds a callback to be called at the beginning of each solver iteration.

Language bindings should call this in the Problem constructor to register callbacks that shouldn't be removed by clear_callbacks(). Persistent callbacks run after non-persistent callbacks.

Parameters

callback

The callback. Returning true from the callback causes the solver to exit early with the solution it has so far.

clear_callbacks()

template<typename Scalar >

void slp::Problem< Scalar >::clear_callbacks ( )

inline

Clears the registered callbacks.

cost_function_type()

template<typename Scalar >

ExpressionType slp::Problem< Scalar >::cost_function_type ( ) const

inline

Returns the cost function's type.

Returns

The cost function's type.

decision_variable() [1/2]

template<typename Scalar >

Variable< Scalar > slp::Problem< Scalar >::decision_variable ( )

inline

Create a decision variable in the optimization problem.

Returns

A decision variable in the optimization problem.

decision_variable() [2/2]

template<typename Scalar >

VariableMatrix< Scalar > slp::Problem< Scalar >::decision_variable ( int  rows, int  cols = 1  )

inline

Create a matrix of decision variables in the optimization problem.

Parameters

rows	Number of matrix rows.
cols	Number of matrix columns.

Returns

A matrix of decision variables in the optimization problem.

equality_constraint_type()

template<typename Scalar >

ExpressionType slp::Problem< Scalar >::equality_constraint_type ( ) const

inline

Returns the type of the highest order equality constraint.

Returns

The type of the highest order equality constraint.

inequality_constraint_type()

template<typename Scalar >

ExpressionType slp::Problem< Scalar >::inequality_constraint_type ( ) const

inline

Returns the type of the highest order inequality constraint.

Returns

The type of the highest order inequality constraint.

maximize() [1/2]

template<typename Scalar >

void slp::Problem< Scalar >::maximize ( const Variable< Scalar > &  objective )

inline

Tells the solver to maximize the output of the given objective function.

Note that this is optional. If only constraints are specified, the solver will find the closest solution to the initial conditions that's in the feasible set.

Parameters

objective

The objective function to maximize.

maximize() [2/2]

template<typename Scalar >

void slp::Problem< Scalar >::maximize ( Variable< Scalar > &&  objective )

inline

Tells the solver to maximize the output of the given objective function.

Note that this is optional. If only constraints are specified, the solver will find the closest solution to the initial conditions that's in the feasible set.

Parameters

objective

The objective function to maximize.

minimize() [1/2]

template<typename Scalar >

void slp::Problem< Scalar >::minimize ( const Variable< Scalar > &  cost )

inline

Tells the solver to minimize the output of the given cost function.

Note that this is optional. If only constraints are specified, the solver will find the closest solution to the initial conditions that's in the feasible set.

Parameters

cost	The cost function to minimize.

minimize() [2/2]

template<typename Scalar >

void slp::Problem< Scalar >::minimize ( Variable< Scalar > &&  cost )

inline

Tells the solver to minimize the output of the given cost function.

Note that this is optional. If only constraints are specified, the solver will find the closest solution to the initial conditions that's in the feasible set.

Parameters

cost	The cost function to minimize.

solve()

template<typename Scalar >

ExitStatus slp::Problem< Scalar >::solve ( const Options &  options = Options{}, bool  spy = false  )

inline

Solve the optimization problem. The solution will be stored in the original variables used to construct the problem.

Parameters

options	Solver options.
spy	Enables writing sparsity patterns of H, Aₑ, and Aᵢ to files named H.spy, A_e.spy, and A_i.spy respectively during solve. Use tools/spy.py to plot them.

Returns

The solver status.

subject_to() [1/4]

template<typename Scalar >

void slp::Problem< Scalar >::subject_to ( const EqualityConstraints< Scalar > &  constraint )

inline

Tells the solver to solve the problem while satisfying the given equality constraint.

Parameters

constraint

The constraint to satisfy.

subject_to() [2/4]

template<typename Scalar >

void slp::Problem< Scalar >::subject_to ( const InequalityConstraints< Scalar > &  constraint )

inline

Tells the solver to solve the problem while satisfying the given inequality constraint.

Parameters

constraint

The constraint to satisfy.

subject_to() [3/4]

template<typename Scalar >

void slp::Problem< Scalar >::subject_to ( EqualityConstraints< Scalar > &&  constraint )

inline

Tells the solver to solve the problem while satisfying the given equality constraint.

Parameters

constraint

The constraint to satisfy.

subject_to() [4/4]

template<typename Scalar >

void slp::Problem< Scalar >::subject_to ( InequalityConstraints< Scalar > &&  constraint )

inline

Tells the solver to solve the problem while satisfying the given inequality constraint.

Parameters

constraint

The constraint to satisfy.

symmetric_decision_variable()

template<typename Scalar >

VariableMatrix< Scalar > slp::Problem< Scalar >::symmetric_decision_variable ( int  rows )

inline

Create a symmetric matrix of decision variables in the optimization problem.

Variable instances are reused across the diagonal, which helps reduce problem dimensionality.

Parameters

rows	Number of matrix rows.

Returns

A symmetric matrix of decision varaibles in the optimization problem.

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

• include/sleipnir/optimization/problem.hpp