USUAL_CONSTRAINTS::Dict

Dictionary that contains all the usual constraints defined in Constraint.jl. It is based on XCSP3-core specifications available at https://arxiv.org/abs/2009.00514

Adding a new constraint is as simple as defining a new function with the same name as the constraint and using the @usual macro to define it. The macro will take care of adding the new constraint to the USUAL_CONSTRAINTS dictionary.

Example

@usual concept_all_different(x; vals=nothing) = xcsp_all_different(list=x, except=vals)

USUAL_SYMMETRIES

A Dictionary that contains the function to apply for each symmetry to avoid searching a whole space.

Constraint

Parametric structure with the following fields.

• concept: a Boolean function that, given an assignment x, outputs true if x satisfies the constraint, and false otherwise.
• error: a positive function that works as preferences over invalid assignments. Return 0.0 if the constraint is satisfied, and a strictly positive real otherwise.

extract_parameters(s::Symbol, constraints_dict=USUAL_CONSTRAINTS; parameters=ConstraintCommons.USUAL_CONSTRAINT_PARAMETERS)

Return the parameters of the constraint s in constraints_dict.

Arguments

• s::Symbol: the constraint name.
• constraints_dict::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.
• parameters::Vector{Symbol}: vector of parameters. Default is ConstraintCommons.USUAL_CONSTRAINT_PARAMETERS.

Example

extract_parameters(:all_different)

args(c::Constraint)

Return the expected length restriction of the arguments in a constraint c. The value nothing indicates that any strictly positive number of value is accepted.

concept(c::Constraint)

Return the concept (function) of constraint c. concept(c::Constraint, x...; param = nothing) Apply the concept of c to values x and optionally param.

concept(s::Symbol, args...; kargs...)

Return the concept of the constraint s applied to args and kargs. This is a shortcut for concept(USUAL_CONSTRAINTS[s])(args...; kargs...).

Arguments

• s::Symbol: the constraint name.
• args...: the arguments to apply the concept to.
• kargs...: the keyword arguments to apply the concept to.

Example

concept(:all_different, [1, 2, 3])

concept_vs_error(c, e, args...; kargs...)

Compare the results of a concept function and an error function for the same inputs. It is mainly used for testing purposes.

Arguments

• c: The concept function.
• e: The error function.
• args...: Positional arguments to be passed to both the concept and error functions.
• kargs...: Keyword arguments to be passed to both the concept and error functions.

Returns

• Boolean: Returns true if the result of the concept function is not equal to whether the result of the error function is greater than 0.0. Otherwise, it returns false.

Examples

concept_vs_error(all_different, make_error(:all_different), [1, 2, 3]) # Returns false

constraints_descriptions(C=USUAL_CONSTRAINTS)

Return a pretty table with the descriptions of the constraints in C.

Arguments

• C::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

Example

constraints_descriptions()

constraints_parameters(C=USUAL_CONSTRAINTS)

Return a pretty table with the parameters of the constraints in C.

Arguments

• C::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

Example

constraints_parameters()

describe(constraints::Dict{Symbol,Constraint}=USUAL_CONSTRAINTS; width=150)

Return a pretty table with the description of the constraints in constraints.

Arguments

• constraints::Dict{Symbol,Constraint}: dictionary of constraints to describe. Default is USUAL_CONSTRAINTS.
• width::Int: width of the table.

Example

describe()

error_f(c::Constraint)

Return the error function of constraint c. error_f(c::Constraint, x; param = nothing) Apply the error function of c to values x and optionally param.

make_error(symb::Symbol)

Create a function that returns an error based on the predicate of the constraint identified by the symbol provided.

Arguments

• symb::Symbol: The symbol used to determine the error function to be returned. The function first checks if a predicate with the prefix "icn" exists in the Constraints module. If it does, it returns that function. If it doesn't, it checks for a predicate with the prefix "error". If that exists, it returns that function. If neither exists, it returns a function that evaluates the predicate with the prefix "concept_" and returns the negation of its result cast to Float64.

Returns

• Function: A function that takes in a variable x and an arbitrary number of parameters params. The function returns a Float64.

Examples

e = make_error(:all_different)
e([1, 2, 3]) # Returns 0.0
e([1, 1, 3]) # Returns 1.0

params_length(c::Constraint)

Return the expected length restriction of the arguments in a constraint c. The value nothing indicates that any strictly positive number of parameters is accepted.

shrink_concept(s)

Simply delete the concept_ part of symbol or string starting with it. TODO: add a check with a warning if s starts with something different.

symmetries(c::Constraint)

Return the list of symmetries of c.

xcsp_all_different(list::Vector{Int})

Return true if all the values of list are different, false otherwise.

Arguments

• list::Vector{Int}: list of values to check.

Variants

• :all_different: Global constraint ensuring that all the values of x are all different.

concept(:all_different, x; vals)
concept(:all_different)(x; vals)

Examples

c = concept(:all_different)

c([1, 2, 3, 4])
c([1, 2, 3, 1])
c([1, 0, 0, 4]; vals=[0])
c([1, 0, 0, 1]; vals=[0])

xcsp_all_equal(list::Vector{Int}, val::Int)

Return true if all the values of list are equal to val, false otherwise.

Arguments

• list::Vector{Int}: list of values to check.
• val::Int: value to compare to.

Variants

• :all_equal: Global constraint ensuring that all the values of x are all equal.

concept(:all_equal, x; val=nothing, pair_vars=zeros(x), op=+)
concept(:all_equal)(x; val=nothing, pair_vars=zeros(x), op=+)

Examples

c = concept(:all_equal)

c([0, 0, 0, 0])
c([1, 2, 3, 4])
c([3, 2, 1, 0]; pair_vars=[0, 1, 2, 3])
c([0, 1, 2, 3]; pair_vars=[0, 1, 2, 3])
c([1, 2, 3, 4]; op=/, val=1, pair_vars=[1, 2, 3, 4])
c([1, 2, 3, 4]; op=*, val=1, pair_vars=[1, 2, 3, 4])

xcsp_cardinality(list, values, occurs, closed)

Return true if the number of occurrences of the values in values in list satisfies the given condition, false otherwise.

Arguments

• list::Vector{Int}: list of values to check.
• values::Vector{Int}: list of values to check.
• occurs::Vector{Int}: list of occurrences to check.
• closed::Bool: whether the constraint is closed or not.

Variants

• :cardinality: The cardinality constraint, also known as the global cardinality constraint (GCC), is a constraint in constraint programming that restricts the number of times a value can appear in a set of variables.

concept(:cardinality, x; bool=false, vals)
concept(:cardinality)(x; bool=false, vals)

• :cardinality_closed: The closed cardinality constraint, also known as the global cardinality constraint (GCC), is a constraint in constraint programming that restricts the number of times a value can appear in a set of variables. It is closed, meaning that all values in the domain of the variables must be considered.

concept(:cardinality_closed, x; vals)
concept(:cardinality_closed)(x; vals)

• :cardinality_open: The open cardinality constraint, also known as the global cardinality constraint (GCC), is a constraint in constraint programming that restricts the number of times a value can appear in a set of variables. It is open, meaning that only the values in the list of values must be considered.

concept(:cardinality_open, x; vals)
concept(:cardinality_open)(x; vals)

Examples

c = concept(:cardinality)

c([2, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3])
c([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3], bool=false)
c([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3], bool=true)
c([2, 5, 10, 10]; vals=[2 1; 5 1; 10 2])
c([2, 5, 10, 10]; vals=[2 0 1 42; 5 1 3 7; 10 2 3 -4])
c([2, 5, 5, 10]; vals=[2 0 1; 5 1 3; 10 2 3])
c([2, 5, 10, 8]; vals=[2 1; 5 1; 10 2])
c([5, 5, 5, 10]; vals=[2 0 1 42; 5 1 3 7; 10 2 3 -4])

cc = concept(:cardinality_closed)
cc([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3])

co = concept(:cardinality_open)
co([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3])

xcsp_channel(; list)

Return true if the channel constraint is satisfied, false otherwise. The channel constraint establishes a bijective correspondence between two sets of variables. This means that each value in the first set of variables corresponds to a unique value in the second set, and vice versa.

Arguments

• list::Union{AbstractVector, Tuple}: list of values to check.

Variants

• :channel: The channel constraint establishes a bijective correspondence between two sets of variables. This means that each value in the first set of variables corresponds to a unique value in the second set, and vice versa.

concept(:channel, x; dim=1, id=nothing)
concept(:channel)(x; dim=1, id=nothing)

Examples

c = concept(:channel)

c([2, 1, 4, 3])
c([1, 2, 3, 4])
c([2, 3, 1, 4])
c([2, 1, 5, 3, 4, 2, 1, 4, 5, 3]; dim=2)
c([2, 1, 4, 3, 5, 2, 1, 4, 5, 3]; dim=2)
c([false, false, true, false]; id=3)
c([false, false, true, false]; id=1)

xcsp_circuit(; list, size)

Return true if the circuit constraint is satisfied, false otherwise. The circuit constraint is a global constraint used in constraint programming, often in routing problems. It ensures that the values of a list of variables form a circuit, i.e., a sequence where each value is the index of the next value in the sequence, and the sequence eventually loops back to the start.

Arguments

• list::AbstractVector: list of values to check.
• size::Int: size of the circuit.

Variants

• :circuit: The circuit constraint is a global constraint used in constraint programming, often in routing problems. It ensures that the values of a list of variables form a circuit, i.e., a sequence where each value is the index of the next value in the sequence, and the sequence eventually loops back to the start.

concept(:circuit, x; op, val)
concept(:circuit)(x; op, val)

Examples

c = concept(:circuit)

c([1, 2, 3, 4])
c([2, 3, 4, 1])
c([2, 3, 1, 4]; op = ==, val = 3)
c([4, 3, 1, 3]; op = >, val = 0)

xcsp_count(list, values, condition)

Return true if the number of occurrences of the values in values in list satisfies the given condition, false otherwise.

Arguments

• list::Vector{Int}: list of values to check.
• values::Vector{Int}: list of values to check.
• condition: condition to satisfy.

Variants

• :count: Constraint ensuring that the number of occurrences of the values in vals in x satisfies the given condition.

concept(:count, x; vals, op, val)
concept(:count)(x; vals, op, val)

• :at_least: Constraint ensuring that the number of occurrences of the values in vals in x is at least val.

concept(:at_least, x; vals, val)
concept(:at_least)(x; vals, val)

• :at_most: Constraint ensuring that the number of occurrences of the values in vals in x is at most val.

concept(:at_most, x; vals, val)
concept(:at_most)(x; vals, val)

• :exactly: Constraint ensuring that the number of occurrences of the values in vals in x is exactly val.

concept(:exactly, x; vals, val)
concept(:exactly)(x; vals, val)

Examples

c = concept(:count)

c([2, 1, 4, 3]; vals=[1, 2, 3, 4], op=≥, val=2)
c([1, 2, 3, 4]; vals=[1, 2], op==, val=2)
c([2, 1, 4, 3]; vals=[1, 2], op=≤, val=1)

xcsp_cumulative(; origins, lengths, heights, condition)

Return true if the cumulative constraint is satisfied, false otherwise. The cumulative constraint is a global constraint used in constraint programming that is often used in scheduling problems. It ensures that for any point in time, the sum of the "heights" of tasks that are ongoing at that time does not exceed a certain limit.

Arguments

• origins::AbstractVector: list of origins of the tasks.
• lengths::AbstractVector: list of lengths of the tasks.
• heights::AbstractVector: list of heights of the tasks.
• condition::Tuple: condition to check.

Variants

• :cumulative: The cumulative constraint is a global constraint used in constraint programming that is often used in scheduling problems. It ensures that for any point in time, the sum of the "heights" of tasks that are ongoing at that time does not exceed a certain limit.

concept(:cumulative, x; pair_vars, op, val)
concept(:cumulative)(x; pair_vars, op, val)

Examples

c = concept(:cumulative)

c([1, 2, 3, 4, 5]; val = 1)
c([1, 2, 2, 4, 5]; val = 1)
c([1, 2, 3, 4, 5]; pair_vars = [3 2 5 4 2; 1 2 1 1 3], op = ≤, val = 5)
c([1, 2, 3, 4, 5]; pair_vars = [3 2 5 4 2; 1 2 1 1 3], op = <, val = 5)

xcsp_element(; list, index, condition)

Return true if the element constraint is satisfied, false otherwise. The element constraint is a global constraint used in constraint programming that specifies that the value of a variable should be equal to the value of another variable indexed by a third variable.

Arguments

• list::Union{AbstractVector, Tuple}: list of values to check.
• index::Int: index of the value to check.
• condition::Tuple: condition to check.

Variants

• :element: The element constraint is a global constraint used in constraint programming that specifies that the value of a variable should be equal to the value of another variable indexed by a third variable.

concept(:element, x; id=nothing, op===, val=nothing)
concept(:element)(x; id=nothing, op===, val=nothing)

Examples

c = concept(:element)

c([1, 2, 3, 4, 5]; id=1, val=1)
c([1, 2, 3, 4, 5]; id=1, val=2)
c([1, 2, 3, 4, 2])
c([1, 2, 3, 4, 1])

xcsp_extension(; list, supports=nothing, conflicts=nothing)

Global constraint enforcing that the tuple x matches a configuration within the supports set pair_vars[1] or does not match any configuration within the conflicts set pair_vars[2]. It embodies the logic: x ∈ pair_vars[1] || x ∉ pair_vars[2], providing a comprehensive way to define valid (supported) and invalid (conflicted) tuples for constraint satisfaction problems. This constraint is versatile, allowing for the explicit delineation of both acceptable and unacceptable configurations.

Arguments

• list::Vector{Int}: A list of variables
• supports::Vector{Vector{Int}}: A set of supported tuples. Default to nothing.
• conflicts::Vector{Vector{Int}}: A set of conflicted tuples. Default to nothing.

Variants

• :extension: Global constraint enforcing that the tuple x matches a configuration within the supports set pair_vars[1] or does not match any configuration within the conflicts set pair_vars[2]. It embodies the logic: x ∈ pair_vars[1] || x ∉ pair_vars[2], providing a comprehensive way to define valid (supported) and invalid (conflicted) tuples for constraint satisfaction problems. This constraint is versatile, allowing for the explicit delineation of both acceptable and unacceptable configurations.

concept(:extension, x; pair_vars)
concept(:extension)(x; pair_vars)

• :supports: Global constraint ensuring that the tuple x matches a configuration listed within the support set pair_vars. This constraint is derived from the extension model, specifying that x must be one of the explicitly defined supported configurations: x ∈ pair_vars. It is utilized to directly declare the tuples that are valid and should be included in the solution space.

concept(:supports, x; pair_vars)
concept(:supports)(x; pair_vars)

• :conflicts: Global constraint ensuring that the tuple x does not match any configuration listed within the conflict set pair_vars. This constraint, originating from the extension model, stipulates that x must avoid all configurations defined as conflicts: x ∉ pair_vars. It is useful for specifying tuples that are explicitly forbidden and should be excluded from the solution space.

concept(:conflicts, x; pair_vars)
concept(:conflicts)(x; pair_vars)

Examples

c = concept(:extension)
c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 3, 4, 5]])
c([1, 2, 3, 4, 5]; pair_vars=([[1, 2, 3, 4, 5]], [[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]]))
c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]])

c = concept(:supports)
c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 3, 4, 5]])

c = concept(:conflicts)
c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]])

xcsp_instantiation(; list, values)

Return true if the instantiation constraint is satisfied, false otherwise. The instantiation constraint is a global constraint used in constraint programming that ensures that a list of variables takes on a specific set of values in a specific order.

Arguments

• list::AbstractVector: list of values to check.
• values::AbstractVector: list of values to check against.

Variants

• :instantiation: The instantiation constraint is a global constraint used in constraint programming that ensures that a list of variables takes on a specific set of values in a specific order.

concept(:instantiation, x; pair_vars)
concept(:instantiation)(x; pair_vars)

Examples

c = concept(:instantiation)

c([1, 2, 3, 4, 5]; pair_vars=[1, 2, 3, 4, 5])
c([1, 2, 3, 4, 5]; pair_vars=[1, 2, 3, 4, 6])

xcsp_intension(list, predicate)

An intensional constraint is usually defined from a predicate over list. As such it encompass any generic constraint.

Arguments

• list::Vector{Int}: A list of variables
• predicate::Function: A predicate over list

Variants

• :dist_different: A constraint ensuring that the distances between marks on the ruler are unique. Specifically, it checks that the distance between x[1] and x[2], and the distance between x[3] and x[4], are different. This constraint is fundamental in ensuring the validity of a Golomb ruler, where no two pairs of marks should have the same distance between them.

concept(:dist_different, x)
concept(:dist_different)(x)

Examples

2 + 2

2 + 2

using Constraints # hide
c = concept(:dist_different)
c([1, 2, 3, 3]) && !c([1, 2, 3, 4])

using Constraints # hide
c = concept(:dist_different)
c([1, 2, 3, 3]) && !c([1, 2, 3, 4])

xcsp_maximum(; list, condition)

Return true if the maximum constraint is satisfied, false otherwise. The maximum constraint is a global constraint used in constraint programming that specifies that a certain condition should hold for the maximum value in a list of variables.

Arguments

• list::Union{AbstractVector, Tuple}: list of values to check.
• condition::Tuple: condition to check.

Variants

• :maximum: The maximum constraint is a global constraint used in constraint programming that specifies that a certain condition should hold for the maximum value in a list of variables.

concept(:maximum, x; op, val)
concept(:maximum)(x; op, val)

Examples

c = concept(:maximum)

c([1, 2, 3, 4, 5]; op = ==, val = 5)
c([1, 2, 3, 4, 5]; op = ==, val = 6)

xcsp_mdd(; list, diagram)

Return a function that checks if the list of values list satisfies the MDD diagram.

Arguments

• list::Vector{Int}: list of values to check.
• diagram::MDD: MDD to check.

Variants

• :mdd: Multi-valued Decision Diagram (MDD) constraint.

  The MDD constraint is a constraint that can be used to model a wide range of problems. It is a directed graph where each node is labeled with a value and each edge is labeled with a value. The constraint is satisfied if there is a path from the first node to the last node such that the sequence of edge labels is a valid sequence of the value labels.

concept(:mdd, x; language)
concept(:mdd)(x; language)

Examples

c = concept(:mdd)

states = [
    Dict( # level x1
        (:r, 0) => :n1,
        (:r, 1) => :n2,
        (:r, 2) => :n3,
    ),
    Dict( # level x2
        (:n1, 2) => :n4,
        (:n2, 2) => :n4,
        (:n3, 0) => :n5,
    ),
    Dict( # level x3
        (:n4, 0) => :t,
        (:n5, 0) => :t,
    ),
]

a = MDD(states)

c([0,2,0]; language = a)
c([1,2,0]; language = a)
c([2,0,0]; language = a)
c([2,1,2]; language = a)
c([1,0,2]; language = a)
c([0,1,2]; language = a)

xcsp_minimum(; list, condition)

Return true if the minimum constraint is satisfied, false otherwise. The minimum constraint is a global constraint used in constraint programming that specifies that a certain condition should hold for the minimum value in a list of variables.

Arguments

• list::Union{AbstractVector, Tuple}: list of values to check.
• condition::Tuple: condition to check.

Variants

• :minimum: The minimum constraint is a global constraint used in constraint programming that specifies that a certain condition should hold for the minimum value in a list of variables.

concept(:minimum, x; op, val)
concept(:minimum)(x; op, val)

Examples

c = concept(:minimum)

c([1, 2, 3, 4, 5]; op = ==, val = 1)
c([1, 2, 3, 4, 5]; op = ==, val = 0)

xcsp_no_overlap(; origins, lengths, zero_ignored)

Return true if the nooverlap constraint is satisfied, false otherwise. The nooverlap constraint is a global constraint used in constraint programming, often in scheduling problems. It ensures that tasks do not overlap in time, i.e., for any two tasks, either the first task finishes before the second task starts, or the second task finishes before the first task starts.

Arguments

• origins::AbstractVector: list of origins of the tasks.
• lengths::AbstractVector: list of lengths of the tasks.
• zero_ignored::Bool: whether to ignore zero-length tasks.

Variants

• :no_overlap: The no_overlap constraint is a global constraint used in constraint programming, often in scheduling problems. It ensures that tasks do not overlap in time, i.e., for any two tasks, either the first task finishes before the second task starts, or the second task finishes before the first task starts.

concept(:no_overlap, x; pair_vars, bool)
concept(:no_overlap)(x; pair_vars, bool)

• :no_overlap_no_zero: The no_overlap constraint is a global constraint used in constraint programming, often in scheduling problems. It ensures that tasks do not overlap in time, i.e., for any two tasks, either the first task finishes before the second task starts, or the second task finishes before the first task starts. This variant ignores zero-length tasks.

concept(:no_overlap_no_zero, x; pair_vars)
concept(:no_overlap_no_zero)(x; pair_vars)

• :no_overlap_with_zero: The no_overlap constraint is a global constraint used in constraint programming, often in scheduling problems. It ensures that tasks do not overlap in time, i.e., for any two tasks, either the first task finishes before the second task starts, or the second task finishes before the first task starts. This variant includes zero-length tasks.

concept(:no_overlap_with_zero, x; pair_vars)
concept(:no_overlap_with_zero)(x; pair_vars)

Examples

c = concept(:no_overlap)

c([1, 2, 3, 4, 5])
c([1, 2, 3, 4, 1])
c([1, 2, 4, 6, 3]; pair_vars = [1, 1, 1, 1, 1])
c([1, 2, 4, 6, 3]; pair_vars = [1, 1, 1, 3, 1])
c([1, 2, 4, 6, 3]; pair_vars = [1, 1, 3, 1, 1])
c([1, 1, 1, 3, 5, 2, 7, 7, 5, 12, 8, 7]; pair_vars = [2, 4, 1, 4 ,2 ,3, 5, 1, 2, 3, 3, 2], dim = 3)
c([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]; pair_vars = [2, 4, 1, 4 ,2 ,3, 5, 1, 2, 3, 3, 2], dim = 3)

xcsp_nvalues(list, condition, except)

Return true if the number of distinct values in list satisfies the given condition, false otherwise.

Arguments

• list::Vector{Int}: list of values to check.
• condition: condition to satisfy.
• except::Union{Nothing, Vector{Int}}: list of values to exclude. Default is nothing.

Variants

• :nvalues: The nValues constraint specifies that the number of distinct values in the list of variables x is equal to a given value. The constraint is defined by the following expression: nValues(x, op, val) where x is a list of variables, op is a comparison operator, and val is an integer value.

concept(:nvalues, x; op, val)
concept(:nvalues)(x; op, val)

Examples

c = concept(:nvalues)

c([1, 2, 3, 4, 5]; op = ==, val = 5)
c([1, 2, 3, 4, 5]; op = ==, val = 2)
c([1, 2, 3, 4, 3]; op = <=, val = 5)
c([1, 2, 3, 4, 3]; op = <=, val = 3)

xcsp_ordered(list::Vector{Int}, operator, lengths)

Return true if all the values of list are in an increasing order, false otherwise.

Arguments

• list::Vector{Int}: list of values to check.
• operator: comparison operator to use.
• lengths: list of lengths to use. Defaults to nothing.

Variants

• :ordered: Global constraint ensuring that all the values of x are in an increasing order.

concept(:ordered, x; op=≤, pair_vars=nothing)
concept(:ordered)(x; op=≤, pair_vars=nothing)

• :increasing: Global constraint ensuring that all the values of x are in an increasing order.

concept(:increasing, x; op=≤, pair_vars=nothing)
concept(:increasing)(x; op=≤, pair_vars=nothing)

• :decreasing: Global constraint ensuring that all the values of x are in a decreasing order.

concept(:decreasing, x; op=≥, pair_vars=nothing)
concept(:decreasing)(x; op=≥, pair_vars=nothing)

• :strictly_increasing: Global constraint ensuring that all the values of x are in a strictly increasing order.

concept(:strictly_increasing, x; op=<, pair_vars=nothing)
concept(:strictly_increasing)(x; op=<, pair_vars=nothing)

• :strictly_decreasing: Global constraint ensuring that all the values of x are in a strictly decreasing order.

concept(:strictly_decreasing, x; op=>, pair_vars=nothing)
concept(:strictly_decreasing)(x; op=>, pair_vars=nothing)

Examples

c = concept(:ordered)

c([1, 2, 3, 4, 4]; op=≤)
c([1, 2, 3, 4, 5]; op=<)
!c([1, 2, 3, 4, 3]; op=≤)
!c([1, 2, 3, 4, 3]; op=<)

xcsp_regular(; list, automaton)

Ensures that a sequence `x` (interpreted as a word) is accepted by the regular language represented by a given automaton. This constraint verifies the compliance of `x` with the language rules encoded within the `automaton` parameter, which must be an instance of `<:AbstractAutomaton`.

Arguments

• list::Vector{Int}: A list of variables
• automaton<:AbstractAutomaton: An automaton representing the regular language

Variants

• :regular: Ensures that a sequence x (interpreted as a word) is accepted by the regular language represented by a given automaton. This constraint verifies the compliance of x with the language rules encoded within the automaton parameter, which must be an instance of <:AbstractAutomaton.

concept(:regular, x; language)
concept(:regular)(x; language)

Examples

c = concept(:regular)

states = Dict(
    (:a, 0) => :a,
    (:a, 1) => :b,
    (:b, 1) => :c,
    (:c, 0) => :d,
    (:d, 0) => :d,
    (:d, 1) => :e,
    (:e, 0) => :e,
)
start = :a
finish = :e

a = Automaton(states, start, finish)

c([0,0,1,1,0,0,1,0,0]; language = a)
c([1,1,1,0,1]; language = a)

xcsp_sum(list, coeffs, condition)

Return true if the sum of the variables in list satisfies the given condition, false otherwise.

Arguments

• list::Vector{Int}: list of values to check.
• coeffs::Vector{Int}: list of coefficients to use.
• condition: condition to satisfy.

Variants

• :sum: Global constraint ensuring that the sum of the variables in x satisfies a given condition.

concept(:sum, x; op===, pair_vars=ones(x), val)
concept(:sum)(x; op===, pair_vars=ones(x), val)

Examples

c = concept(:sum)

c([1, 2, 3, 4, 5]; op===, val=15)
c([1, 2, 3, 4, 5]; op===, val=2)
c([1, 2, 3, 4, 3]; op=≤, val=15)
c([1, 2, 3, 4, 3]; op=≤, val=3)

usual(ex::Expr)

This macro is used to define a new constraint or update an existing one in the USUAL_CONSTRAINTS dictionary. It takes an expression ex as input, which represents the definition of a constraint.

Here's a step-by-step explanation of what the macro does:

1. It first extracts the symbol of the concept from the input expression. This symbol is expected to be the first argument of the first argument of the expression. For example, if the expression is @usual alldifferent(x; y=1), the symbol would be :alldifferent.
2. It then calls the shrink_concept function on the symbol to get a simplified version of the concept symbol.
3. It initializes a dictionary defaults to store whether each keyword argument of the concept has a default value or not.
4. It checks if the expression has more than two arguments. If it does, it means that there are keyword arguments present. It then loops over these keyword arguments. If a keyword argument is a symbol, it means it doesn't have a default value, so it adds an entry to the defaults dictionary with the keyword argument as the key and false as the value. If a keyword argument is not a symbol, it means it has a default value, so it adds an entry to the defaults dictionary with the keyword argument as the key and true as the value.
5. It calls the make_error function on the simplified concept symbol to generate an error function for the constraint.
6. It evaluates the input expression to get the concept function.
7. It checks if the USUALCONSTRAINTS dictionary already contains an entry for the simplified concept symbol. If it does, it adds the defaults dictionary to the parameters of the existing constraint. If it doesn't, it creates a new constraint with the concept function, a description, the error function, and the defaults dictionary as the parameters, and adds it to the USUALCONSTRAINTS dictionary.

This macro is used to make it easier to define and update constraints in a consistent and possibly automated way.

Arguments

• ex::Expr: expression to parse.

Example

@usual concept_all_different(x; vals=nothing) = xcsp_all_different(list=x, except=vals)