idol::Robust::AffineDecisionRule Class Reference

Inheritance diagram for idol::Robust::AffineDecisionRule:

Collaboration diagram for idol::Robust::AffineDecisionRule:

Classes
struct	Result

Public Member Functions
	AffineDecisionRule (const Robust::Description &t_description)
	AffineDecisionRule (const AffineDecisionRule &t_src)
Optimizer *	operator() (const Model &t_model) const override
OptimizerFactory *	clone () const override
AffineDecisionRule &	with_deterministic_optimizer (const OptimizerFactory &t_deterministic_optimizer)
AffineDecisionRule &	with_logs (bool t_value)
AffineDecisionRule &	with_time_limit (double t_time_limit)
AffineDecisionRule &	with_thread_limit (unsigned int t_max_n_threads)
AffineDecisionRule &	with_iteration_limit (unsigned int t_iteration_count_limit)
AffineDecisionRule &	with_best_bound_stop (double t_best_bound_stop)
AffineDecisionRule &	with_best_obj_stop (double t_user_best_obj)
AffineDecisionRule &	with_relative_gap_tolerance (double t_relative_gap_tolerance)
AffineDecisionRule &	with_absolute_gap_tolerance (double t_absolute_gap_tolerance)
AffineDecisionRule &	with_presolve (bool t_value)
AffineDecisionRule &	with_infeasible_or_unbounded_info (bool t_value)
AffineDecisionRule &	conditional (bool t_conditional_value, const std::function< void(AffineDecisionRule &)> &t_if)
AffineDecisionRule &	conditional (bool t_conditional_value, const std::function< void(AffineDecisionRule &)> &t_if, const std::function< void(AffineDecisionRule &)> &t_else)
template<class T >
T &	as ()
template<class T >
const T &	as () const
template<class T >
bool	is () const

Static Public Member Functions
static Result	make_model (const Model &t_model, const Robust::Description &t_description)

Protected Member Functions
AffineDecisionRule &	crtp ()
const AffineDecisionRule &	crtp () const
void	handle_default_parameters (Optimizer *t_optimizer) const

Detailed Description

Definition at line 16 of file AffineDecisionRule.h.

Member Function Documentation

as() [1/2]

template<class T >

T & idol::OptimizerFactory::as ( )

inlineinherited

Definition at line 44 of file OptimizerFactory.h.

as() [2/2]

template<class T >

const T & idol::OptimizerFactory::as ( ) const

inlineinherited

Definition at line 52 of file OptimizerFactory.h.

clone()

OptimizerFactory * idol::Robust::AffineDecisionRule::clone ( ) const

overridevirtual

Creates and return a copy of the optimizer factory. This is used for polymorphism.

Returns

A copied object of the current object (i.e., *this)

Implements idol::OptimizerFactory.

conditional() [1/2]

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::conditional ( bool  t_conditional_value, const std::function< void(AffineDecisionRule &)> &  t_if  )

inherited

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

Example:

for (const bool use_presolve : {true, false}) {
     auto algorithm = GLPK()
                         .conditional(use_presolve, [](auto& x){ x.with_presolve(true); })
      model.use(algorithm);
      model.optimize();
}

Parameters

t_conditional_value	if true, the t_if lambda function is executed, if false, nothing happens.
t_if	lambda function to execute in case t_conditional_value is true

Returns

the optimizer factory itself

Definition at line 238 of file OptimizerFactory.h.

conditional() [2/2]

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::conditional ( bool  t_conditional_value, const std::function< void(AffineDecisionRule &)> &  t_if, const std::function< void(AffineDecisionRule &)> &  t_else  )

inherited

Executes the lambda function given as second parameter if and only if its first argument is true. This function can be used to build different optimizer factories depending on some external variable.

Example:

for (const bool use_presolve : {true, false}) {
     auto algorithm = GLPK()
                         .conditional(use_presolve,
                                         [](auto& x){ x.with_presolve(true); },
                                         [](auto& x){ x.with_presolve(false); })
      model.use(algorithm);
      model.optimize();
}

Parameters

t_conditional_value	if true, the t_if lambda function is executed, if false, the t_else lambda function is.
t_if	lambda function to execute in case t_conditional_value is true
t_else	lambda function to execute in case t_conditional_value is false

Returns

the optimizer factory itself

Definition at line 260 of file OptimizerFactory.h.

crtp() [1/2]

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::crtp ( )

inlineprotectedinherited

Definition at line 78 of file OptimizerFactory.h.

crtp() [2/2]

const AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::crtp ( ) const

inlineprotectedinherited

Definition at line 79 of file OptimizerFactory.h.

handle_default_parameters()

void idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::handle_default_parameters ( Optimizer *  t_optimizer ) const

protectedinherited

Definition at line 81 of file OptimizerFactory.h.

is()

template<class T >

bool idol::OptimizerFactory::is ( ) const

inlineinherited

Definition at line 60 of file OptimizerFactory.h.

operator()()

Optimizer * idol::Robust::AffineDecisionRule::operator() ( const Model &  t_model ) const

overridevirtual

Creates and returns a new optimizer to solve the model given as parameter.

Parameters

t_model

The model which the optimizer will solve

Returns

A new optimizer for the model

Implements idol::OptimizerFactory.

with_absolute_gap_tolerance()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_absolute_gap_tolerance ( double  t_absolute_gap_tolerance )

inherited

Sets the absolute gap tolerance for the optimizer. When the optimizer proves that the absolute optimality gap is less than this threshold, the optimizer stops.

Example:

auto algorithm = GLPK()
                     .with_absolute_gap_tolerance(1e-4);

Parameters

t_absolute_gap_tolerance

the absolute gap tolerance

Returns

the optimizer factory itself

Definition at line 192 of file OptimizerFactory.h.

with_best_bound_stop()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_best_bound_stop ( double  t_best_bound_stop )

inherited

Sets a threshold on the best bound for stopping the optimizer. When the optimizer have found a best bound which is greater than this threshold, the optimizer stops.

Example:

const double my_known_best_obj = 0.;
auto algorithm = GLPK()
                     .with_best_bound_stop(my_known_best_obj);

Parameters

t_best_bound_stop

the threshold

Returns

the optimizer factory itself

Definition at line 149 of file OptimizerFactory.h.

with_best_obj_stop()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_best_obj_stop ( double  t_user_best_obj )

inherited

Sets a threshold on the best objective value for stopping the optimizer. When the optimizer have found a best objective value which is less than this threshold, the optimizer stops.

Example:

const double my_known_best_bound = 0;
auto algorithm = GLPK()
                     .with_best_obj_stop(my_known_best_bound);

Parameters

t_user_best_obj

the threshold

Returns

the optimizer factory itself

Definition at line 164 of file OptimizerFactory.h.

with_infeasible_or_unbounded_info()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_infeasible_or_unbounded_info ( bool  t_value )

inherited

Sets the behaviour of the optimizer when a model is shown to be infeasible or unbounded. When set to true, the optimizer is forced to prove feasibility or unboundedness by providing a certificate.

Example:

auto algorithm = GLPK()
                     .with_infeasible_or_unbounded_info(true);

Parameters

t_value

the activation level

Returns

the optimizer factory itself

Definition at line 219 of file OptimizerFactory.h.

with_iteration_limit()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_iteration_limit ( unsigned int  t_iteration_count_limit )

inherited

Sets the maximum number of iterations which the optimizer go through

Example:

auto algorithm = GLPK()
                     .with_iteration_limit(200);

Parameters

t_iteration_count_limit

the maximum number of iterations

Returns

the optimizer factory itself

Definition at line 134 of file OptimizerFactory.h.

with_logs()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_logs ( bool  t_value )

inherited

Sets the log_master level and color for the optimizer

Example:

auto algorithm = GLPK()
                     .with_logs(true);

Parameters

t_log_level	the log_master level
t_log_color	the output color

Returns

the optimizer factory itself

Definition at line 95 of file OptimizerFactory.h.

with_presolve()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_presolve ( bool  t_value )

inherited

Sets the get_param_presolve activation for the optimizer.

Example:

auto algorithm = GLPK()
                     .with_presolve(false); // turns off get_param_presolve phase

Parameters

t_value

the activation level for the optimizer's get_param_presolve (0 for disabling, 1 for enabling)

Returns

the optimizer factory itself

Definition at line 205 of file OptimizerFactory.h.

with_relative_gap_tolerance()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_relative_gap_tolerance ( double  t_relative_gap_tolerance )

inherited

Sets the relative gap tolerance for the optimizer. When the optimizer proves that the relative optimality gap is less than this threshold, the optimizer stops.

Example:

auto algorithm = GLPK()
                     .with_relative_gap_tolerance(.05); // sets a gap tolerance of 5%

Parameters

t_relative_gap_tolerance

the relative gap tolerance

Returns

the optimizer factory itself

Definition at line 178 of file OptimizerFactory.h.

with_thread_limit()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_thread_limit ( unsigned int  t_max_n_threads )

inherited

Sets the maximum number of threads which the optimizer can use

Example:

auto algorithm = GLPK()
                     .with_thread_limit(5);

Parameters

t_max_n_threads

the number of threads which can be used

Returns

the optimizer factory itself

Definition at line 121 of file OptimizerFactory.h.

with_time_limit()

AffineDecisionRule & idol::OptimizerFactoryWithDefaultParameters< AffineDecisionRule >::with_time_limit ( double  t_time_limit )

inherited

Sets the time limit for the optimizer

Example:

auto algorithm = GLPK()
                     .with_time_limit(3600);

Parameters

t_time_limit

the time limit (in seconds)

Returns

the optimizer factory itself

Definition at line 108 of file OptimizerFactory.h.