IntegerStarDiscrepancy

class ioh.iohcpp.problem.IntegerStarDiscrepancy(self: ioh.iohcpp.problem.IntegerStarDiscrepancy, instance: int = 1, n_variables: int = 5, n_samples: int = 5, sampler_type: ioh.iohcpp.problem.StarDiscrepancySampler = <StarDiscrepancySampler.UNIFORM: 0>)

Bases: IntegerSingleObjective

Star-discrepancy problems

The 𝐿_infinity star discrepancy is a measure for the regularity of a finite set of points taken from [0, 1)^d. Calculating this measure is challenging, and exact algorithm fall short even for relatively small grids. This suite provides access to a set of these problems to tackle the problem from an optimization perspective.

There are two equivalent representations for these problems: Integer-based and Real-valued.

For the Real-valued, the goal is to find the a point in [0,1)^d which minimizes the resulting discepancy directly.

The Integer-valued version takes advantage of the fact that for each dimension, the optimium has a value matching an existing point int the grid. As such, the search for a point in the grid can be reduced to finding in each dimension the index of the coordinate, resulting in a search domain [0,n]^d, where n is the number of gridpoints.

Reference

[Clement23] Clement, Francois, Diederick Vermetten, Jacob de Nobel, Alexandre Jesus, Luís Paquete, and Carola Doerr. “Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms.”

Star Discrepancy Problems

param instance:

The instance of the problem, will be used as seed for sampler

type instance:

int = 1

param n_variables:

The dimension of the problem

type n_variables:

int = 5

param n_samples:

The number of sampled points

type n_samples:

int = 5

param sampler_type:

The type of sampler to use when sampling the points

type sampler_type:

StarDiscrepancySampler

Attributes Summary

bounds	The bounds of the problem.
constraints	The constraints of the problem.
grid
log_info	The data is that being sent to the logger.
meta_data	The static meta-data of the problem containing, e.g., problem id, instance id, and problem's dimensionality
optimum	The optimum and its objective value for a problem instance
problems
state	The current state of the optimization process containing, e.g., the current solution and the number of function evaluated consumed so far

Methods Summary

__call__	Evaluate the problem.
add_constraint	add a constraint
attach_logger	Attach a logger to the problem to allow performance tracking.
create(*args, **kwargs)	Overloaded function.
detach_logger	Remove the specified logger from the problem.
enforce_bounds	Enforced the bounds (box-constraints) as constraint :param weight: :type weight: The weight for computing the penalty (can be infinity to have strict box-constraints) :param how: :type how: The enforcement strategy, should be one of the 'ioh.ConstraintEnforcement' options :param exponent: :type exponent: The exponent for scaling the contraint
remove_constraint	remove a constraint
reset	Reset all state variables of the problem.
set_id	update the problem id
set_instance	update the problem instance
set_name	update the problem name

Attributes Documentation

bounds

The bounds of the problem.

constraints

The constraints of the problem.

grid

log_info

The data is that being sent to the logger.

meta_data

The static meta-data of the problem containing, e.g., problem id, instance id, and problem’s dimensionality

optimum

The optimum and its objective value for a problem instance

problems

state

The current state of the optimization process containing, e.g., the current solution and the number of function evaluated consumed so far

Methods Documentation

__call__()

Evaluate the problem.

Parameters:

x (list) – the search point to evaluate. It must be a 1-dimensional array/list whose length matches search space’s dimensionality

Returns:

The evaluated search point

Return type:

float

Evaluate the problem.

Parameters:

x (list[list]) – the search points to evaluate. It must be a 2-dimensional array/list whose length matches search space’s dimensionality

Returns:

The evaluated search points

Return type:

list[float]

add_constraint()

add a constraint

attach_logger()

Attach a logger to the problem to allow performance tracking.

Parameters:

logger (Logger) – A logger-object from the IOHexperimenter logger module.

static create(*args, **kwargs)

Overloaded function.

1. create(problem_name: str, instance_id: int, dimension: int) -> ioh.iohcpp.problem.IntegerStarDiscrepancy

   Create a problem instance

   problem_name: str

   a string indicating the problem name.

   instance_id: int

   an integer identifier of the problem instance

   dimension: int

   the dimensionality of the search space

2. create(problem_id: int, instance_id: int, dimension: int) -> ioh.iohcpp.problem.IntegerStarDiscrepancy

   Create a problem instance

   problem_name: int

   a string indicating the problem name.

   instance_id: int

   an integer identifier of the problem instance

   dimension: int

   the dimensionality of the search space

detach_logger()

Remove the specified logger from the problem.

enforce_bounds()

Enforced the bounds (box-constraints) as constraint :param weight: :type weight: The weight for computing the penalty (can be infinity to have strict box-constraints) :param how: :type how: The enforcement strategy, should be one of the ‘ioh.ConstraintEnforcement’ options :param exponent: :type exponent: The exponent for scaling the contraint

remove_constraint()

remove a constraint

reset()

Reset all state variables of the problem.

set_id()

update the problem id

set_instance()

update the problem instance

set_name()

update the problem name