Finite Sets

The finite sets theory provides reasoning about finite sets with explicit cardinality tracking, unlike the array-based set encoding. This theory is useful for combinatorial problems and set algebra with bounded domains.

Finite Set Sort

Create a finite set sort over an element type:

from z3 import *

# Note: FiniteSet support may vary by Z3 version/binding
# This documents the C API

# Finite set of integers
IntFiniteSet = FiniteSetSort(IntSort())
S = Const('S', IntFiniteSet)

# Finite set of bit-vectors
BVFiniteSet = FiniteSetSort(BitVecSort(8))
T = Const('T', BVFiniteSet)

C API: Z3_mk_finite_set_sort(c, elem_sort) creates finite set sort (api_finite_set.cpp:27)

Check Sort

Verify if a sort is a finite set: C API: Z3_is_finite_set_sort(c, s) (api_finite_set.cpp:38)

Get Element Sort

Retrieve the element sort of a finite set: C API: Z3_get_finite_set_sort_basis(c, s) (api_finite_set.cpp:46)

Set Construction

Empty Set

Create an empty set:

IntFiniteSet = FiniteSetSort(IntSort())
empty = EmptyFiniteSet(IntFiniteSet)

solve(Size(empty) == 0)
# sat

C API: Z3_mk_finite_set_empty(c, set_sort) (api_finite_set.cpp:59)

Singleton Set

Create a set with one element:

x = Int('x')
singleton = SingletonFiniteSet(x)

solve(x == 42, Size(singleton) == 1)
# sat

C API: Z3_mk_finite_set_singleton(c, elem) (api_finite_set.cpp:69)

Set from Range

Create a set containing all integers in a range:

low = Int('low')
high = Int('high')
range_set = FiniteSetRange(low, high)

solve(
    low == 1,
    high == 10,
    Size(range_set) == 10
)
# [low = 1, high = 10, |range_set| = 10]

C API: Z3_mk_finite_set_range(c, low, high) creates set containing low through high (api_finite_set.cpp:175)

Set Operations

Union

Combine two sets:

S = Const('S', FiniteSetSort(IntSort()))
T = Const('T', FiniteSetSort(IntSort()))

union_set = FiniteSetUnion(S, T)

x = Int('x')
solve(
    IsMemberFiniteSet(x, S),
    Not(IsMemberFiniteSet(x, T)),
    IsMemberFiniteSet(x, union_set)
)
# sat - x is in S, so it's in the union

C API: Z3_mk_finite_set_union(c, s1, s2) (api_finite_set.cpp:80)

Intersection

Get common elements:

intersect_set = FiniteSetIntersect(S, T)

solve(
    Size(intersect_set) > 0,
    Size(S) == 5,
    Size(T) == 5
)
# Sets have at least one element in common

C API: Z3_mk_finite_set_intersect(c, s1, s2) (api_finite_set.cpp:92)

Difference

Remove elements of one set from another:

diff_set = FiniteSetDifference(S, T)

solve(
    Size(S) == 10,
    Size(T) == 3,
    Size(diff_set) == 7
)
# All elements of T are in S

C API: Z3_mk_finite_set_difference(c, s1, s2) (api_finite_set.cpp:104)

Set Predicates

Membership

Test if an element is in a set:

S = Const('S', FiniteSetSort(IntSort()))
x = Int('x')

solve(
    IsMemberFiniteSet(x, S),
    x == 42,
    Size(S) == 1
)
# [S = {42}, x = 42]

C API: Z3_mk_finite_set_member(c, elem, set) (api_finite_set.cpp:116)

Subset

Test if one set is contained in another:

S = Const('S', FiniteSetSort(IntSort()))
T = Const('T', FiniteSetSort(IntSort()))

solve(
    IsSubsetFiniteSet(S, T),
    Size(S) == 3,
    Size(T) == 5,
    Not(S == T)
)
# S ⊆ T and S ≠ T

C API: Z3_mk_finite_set_subset(c, s1, s2) (api_finite_set.cpp:139)

Cardinality

Set Size

Get the number of elements in a set:

S = Const('S', FiniteSetSort(IntSort()))
size = Int('size')

solve(
    size == FiniteSetSize(S),
    size >= 1,
    size <= 10
)
# S has between 1 and 10 elements

C API: Z3_mk_finite_set_size(c, set) returns integer representing cardinality (api_finite_set.cpp:128)

Cardinality Constraints

Reason about set sizes:

S = Const('S', FiniteSetSort(IntSort()))
T = Const('T', FiniteSetSort(IntSort()))
U = FiniteSetUnion(S, T)

solve(
    Size(S) == 5,
    Size(T) == 3,
    Size(FiniteSetIntersect(S, T)) == 2,
    Size(U) == 6  # |S ∪ T| = |S| + |T| - |S ∩ T|
)
# sat

Higher-Order Operations

Map

Apply a function to all elements:

S = Const('S', FiniteSetSort(IntSort()))
f = Function('f', IntSort(), IntSort())

# Map f over S: {f(x) | x ∈ S}
mapped_set = FiniteSetMap(f, S)

solve(
    Size(S) == 3,
    Size(mapped_set) <= 3  # May be smaller if f is not injective
)

C API: Z3_mk_finite_set_map(c, f, set) (api_finite_set.cpp:151)

Filter

Select elements satisfying a predicate:

S = Const('S', FiniteSetSort(IntSort()))
p = Function('p', IntSort(), BoolSort())

# Filter S by predicate: {x ∈ S | p(x)}
filtered_set = FiniteSetFilter(p, S)

solve(
    Size(S) == 10,
    Size(filtered_set) == 5
)
# Half the elements satisfy the predicate

C API: Z3_mk_finite_set_filter(c, f, set) (api_finite_set.cpp:163)

Complete Examples

Example 1: Set Cover Problem

from z3 import *

# Simplified set cover using finite sets
# Universe of elements: {1, 2, 3, 4, 5}
# Available sets: S1={1,2}, S2={2,3}, S3={3,4}, S4={4,5}
# Find minimum number of sets to cover all elements

universe = FiniteSetRange(IntVal(1), IntVal(5))

# Decision variables: include each set or not
use_s1 = Bool('use_s1')
use_s2 = Bool('use_s2')
use_s3 = Bool('use_s3')
use_s4 = Bool('use_s4')

# Build the covering based on selections
covering = EmptyFiniteSet(FiniteSetSort(IntSort()))
# Add sets conditionally (pseudocode - actual encoding more complex)

# Minimize number of sets used
cost = If(use_s1, 1, 0) + If(use_s2, 1, 0) + If(use_s3, 1, 0) + If(use_s4, 1, 0)

solve(
    IsSubsetFiniteSet(universe, covering),  # All elements covered
    cost <= 3  # Use at most 3 sets
)

Example 2: Partition Problem

from z3 import *

# Partition a set into two subsets with equal sum
original = FiniteSetRange(IntVal(1), IntVal(10))
S1 = Const('S1', FiniteSetSort(IntSort()))
S2 = Const('S2', FiniteSetSort(IntSort()))

solve(
    # S1 and S2 partition the original set
    FiniteSetUnion(S1, S2) == original,
    Size(FiniteSetIntersect(S1, S2)) == 0,  # Disjoint
    
    # Equal sums (would need sum function)
    Size(S1) == Size(S2)  # At least equal sizes
)

Example 3: Graph Coloring

from z3 import *

# Color nodes such that no edge connects same-colored nodes
# Graph represented as adjacency

Color = DeclareSort('Color')
Node = DeclareSort('Node')

red = Const('red', Color)
blue = Const('blue', Color)
green = Const('green', Color)

# Sets of nodes for each color
red_nodes = Const('red_nodes', FiniteSetSort(Node))
blue_nodes = Const('blue_nodes', FiniteSetSort(Node))
green_nodes = Const('green_nodes', FiniteSetSort(Node))

# All nodes colored
all_nodes = Const('all_nodes', FiniteSetSort(Node))

solve(
    # Partition nodes by color
    FiniteSetUnion(red_nodes, FiniteSetUnion(blue_nodes, green_nodes)) == all_nodes,
    
    # Colors are disjoint
    Size(FiniteSetIntersect(red_nodes, blue_nodes)) == 0,
    Size(FiniteSetIntersect(red_nodes, green_nodes)) == 0,
    Size(FiniteSetIntersect(blue_nodes, green_nodes)) == 0
    
    # Add edge constraints here
)

Example 4: Combinatorial Constraints

from z3 import *

# Select exactly k elements from a set
elements = FiniteSetRange(IntVal(1), IntVal(10))
selection = Const('selection', FiniteSetSort(IntSort()))
k = Int('k')

solve(
    IsSubsetFiniteSet(selection, elements),
    Size(selection) == k,
    k == 5,
    # Additional constraints on selected elements
    ForAll([x], 
        Implies(
            IsMemberFiniteSet(x, selection),
            x > 3  # Only select elements > 3
        )
    )
)

Example 5: Cardinality-Based Reasoning

from z3 import *

# Prove: |A ∪ B| = |A| + |B| - |A ∩ B|
A = Const('A', FiniteSetSort(IntSort()))
B = Const('B', FiniteSetSort(IntSort()))

solve(
    Size(FiniteSetUnion(A, B)) != 
    Size(A) + Size(B) - Size(FiniteSetIntersect(A, B))
)
# unsat - the formula is a tautology

Example 6: Bounded Quantification

from z3 import *

# All elements in a set satisfy a property
S = Const('S', FiniteSetSort(IntSort()))

# Every element in S is even
solve(
    Size(S) > 0,
    ForAll([x], 
        Implies(
            IsMemberFiniteSet(x, S),
            x % 2 == 0
        )
    ),
    IsMemberFiniteSet(IntVal(3), S)  # Contradiction
)
# unsat - cannot have odd number in set of evens

Advantages Over Array-Based Sets

Explicit Cardinality: Size is a first-class concept
Bounded Domains: Better for finite universe problems
Combinatorial Reasoning: Natural encoding for counting problems
Performance: Can be more efficient for cardinality-heavy constraints

Limitations

Only for finite sets (unbounded sets use array theory)
May not be available in all Z3 bindings (C API most complete)
Element type must have decidable equality

Use Cases

Combinatorial Optimization: Set cover, partition, packing problems
Resource Allocation: Assign items to bins with capacity constraints
Graph Algorithms: Coloring, independent sets, cliques
Database Queries: Set operations with cardinality constraints
Type Systems: Intersection types, type sets

Comparison with Array-Based Sets

Feature	Finite Sets	Array-Based Sets
Cardinality	Direct	Via axioms
Domain	Finite	Potentially infinite
Operations	Set-theoretic	Array select/store
Use Case	Combinatorics	General sets

Implementation Notes

Finite sets compile to constraints on cardinality
Uses specialized decision procedures for set algebra
Cardinality bounds help prune search space
May use BDD-based representations internally

API Summary

C API Functions (api_finite_set.cpp):

Sort: Z3_mk_finite_set_sort, Z3_is_finite_set_sort, Z3_get_finite_set_sort_basis
Construction: Z3_mk_finite_set_empty, Z3_mk_finite_set_singleton, Z3_mk_finite_set_range
Operations: Z3_mk_finite_set_union, Z3_mk_finite_set_intersect, Z3_mk_finite_set_difference
Predicates: Z3_mk_finite_set_member, Z3_mk_finite_set_subset
Cardinality: Z3_mk_finite_set_size
Higher-order: Z3_mk_finite_set_map, Z3_mk_finite_set_filter

References

Implementation: src/api/api_finite_set.cpp
Theory plugin: Finite sets theory
Related: Array theory for infinite sets

Core API

Theory Solvers

Advanced

Finite Set Sort

Check Sort

Get Element Sort

Set Construction

Empty Set

Singleton Set

Set from Range

Set Operations

Union

Intersection

Difference

Set Predicates

Membership

Subset

Cardinality

Set Size

Cardinality Constraints

Higher-Order Operations

Map

Filter

Complete Examples

Example 1: Set Cover Problem

Example 2: Partition Problem

Example 3: Graph Coloring

Example 4: Combinatorial Constraints

Example 5: Cardinality-Based Reasoning

Example 6: Bounded Quantification

Advantages Over Array-Based Sets

Limitations

Use Cases

Comparison with Array-Based Sets

Implementation Notes

API Summary

References

Build docs developers (and LLMs) love

Core API

Theory Solvers

Advanced

Documentation Index

​Finite Set Sort

​Check Sort

​Get Element Sort

​Set Construction

​Empty Set

​Singleton Set

​Set from Range

​Set Operations

​Union

​Intersection

​Difference

​Set Predicates

​Membership

​Subset

​Cardinality

​Set Size

​Cardinality Constraints

​Higher-Order Operations

​Map

​Filter

​Complete Examples

​Example 1: Set Cover Problem

​Example 2: Partition Problem

​Example 3: Graph Coloring

​Example 4: Combinatorial Constraints

​Example 5: Cardinality-Based Reasoning

​Example 6: Bounded Quantification

​Advantages Over Array-Based Sets

​Limitations

​Use Cases

​Comparison with Array-Based Sets

​Implementation Notes

​API Summary

​References

Build docs developers (and LLMs) love

Finite Set Sort

Check Sort

Get Element Sort

Set Construction

Empty Set

Singleton Set

Set from Range

Set Operations

Union

Intersection

Difference

Set Predicates

Membership

Subset

Cardinality

Set Size

Cardinality Constraints

Higher-Order Operations

Map

Filter

Complete Examples

Example 1: Set Cover Problem

Example 2: Partition Problem

Example 3: Graph Coloring

Example 4: Combinatorial Constraints

Example 5: Cardinality-Based Reasoning

Example 6: Bounded Quantification

Advantages Over Array-Based Sets

Limitations

Use Cases

Comparison with Array-Based Sets

Implementation Notes

API Summary

References