Linear Representation of Relational Operations Kenneth A. Presting University of North Carolina at...
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Transcript of Linear Representation of Relational Operations Kenneth A. Presting University of North Carolina at...
Linear Representation of Relational Operations
Kenneth A. Presting
University of North Carolina at Chapel Hill
Relations on a Domain
• Domain is an arbitrary set, Ω
• Relations are subsets of Ωn
• All examples used today take Ωn as ordered tuples of natural numbers,
Ωn = {(ai)1≤i≤n | ai N }
• All definitions and proofs today can extend to arbitrary domains, indexed by ordinals
Graph of a Relation
• We want to study relations extensionally, so we begin from the relation’s graph
• The graph is the set of tuples, in the context of the n-dimensional space
• n-ary relation → set of n-tuples
• Examples:
x2 + y2 = p → points on a circle, in a planez = nx + my + b → points in a plane, in 3-space
Hyperplanes and Lines
• Take an n-dimensional Cartesian product, Ωn, as an abstract coordinate space.
• Then an n-1 dimensional subspace, Ωn-1, is an abstract hyperplane in Ωn.
• For each point (a1,…,an-1) in the hyperplane Ωn-
1, there is an abstract “perpendicular line,” Ω x {(a1,…,an-1)}
Illustration
Graph, Hyperplane, Perpendicular Line, and Slice
Slices of the Graph
• Let F(x1,…,xn) be an n-ary relation• Let the plain symbol F denote its graph:
F = {(x1,…,xn)| F(x1,…,xn)}
• Let a1,…,an-1 be n-1 elements of Ω
• Then for each variable xi there is a setFxi|a1,…,an-1 = { ωΩ | F(a1,…,ai-1,ω,ai,…, an-1}
• This set is the xi’s which satisfy F(…xi…) when all the other variables are fixed
The Matrix of Slices
• Every n-ary relation defines n set-valued functions on n-1 variables:
Fxi(v1,…,vn-1) = { ωΩ | F(v1,…,vi-1,ω,vi,…,vn-1) }
• The n-tuple of these functions is called the “matrix of slices” of the relation F
Properties of the Matrix
• Each slice is a subset of the domain
• Each function Fxi(v1,…,vn-1) : Ωn-1 → 2Ω
maps vectors over the domain to subsets of the domain
• Application to measure theory
Inverse Map: Matrices to Relations
• Two-stage process, one step at a time• Union across columns in each row:
RowF(v1,…,vn-1) =
n| i<j → ai = vj
U { <ai> Ωn | i=j → ai Fxj(v1,…,vn-1) }
j=1 | i>j → ai = vj-1
• Union of n-tuples from every row: F = U<vi>Ωn-1 RowF(v1,…,vn-1)
Properties of the Slicing Maps
• Map from relations to matrices is injective but not surjective
• Inverse map from matrices to relations is surjective but not injective
• Not all matrices in pre-image of a relation follow it homomorphically in operations
Boolean Operations on Matrices
• Matrices treated as vectors
• i.e., Direct Product of Boolean algebras
– Component-wise conjunction
– Component-wise disjunction
– Component-wise complementation
Cylindrical Algebra Operations
• Diagonal Elements– Images of diagonal relations, operate by
logical conjunction with operand relation
• Cylindrifications– Binding a variable with existential quantifier
• Substitutions– Exchange of variables in relational expression
The Diagonal Relations
• Matrix images of an identity relation, xi = xj
• Example. In four dimensions, x2 = x3 maps to:
Index Value of x1
Value of x2
Value of x3
Value of x4
0,0,0 Ω {0} {0} Ω
0,0,1 Ω {0} {0} Ω
0,0, … Ω {0} {0} Ω
0,1,0 Ω {1} {1} Ω
0,1,1 Ω {1} {1} Ω
… Ω … … Ω
Axioms for Diagonals
• Universal Diagonal– dκκ = 1
• Independence– κ {λ,μ} → cκ dλμ = dλμ
• Complementation– κ λ → cκ (dκλ • F) • cκ (dκλ • ~F) = 0
Cylindrical Identity Elements
• 1 is the matrix with all components Ω, i.e. the image of a universal relation such as xi=xi
• 0 is the matrix with all components Ø, i.e. the image of the empty relation
Diagonal Operations are Boolean
• Boolean conjunction of relation matrix with diagonal relation matrix
• Example
Substitution is not Boolean
• Substitution of variables permutes the slices – not a component-wise operation
• Composition of Diagonal with Substitution s
κλ F = cκ ( dκλ • F )
• If we assume Boolean arithmetic, then standard matrix multiplication suffices
Boolean Matrix Multiplication
• Take union down rows, of intersections across columns
Substitution Operators
• Square matrices, indexed by all variables in all relations
• Substitution operator is the elementary matrix operator for exchange of columns
• Example: in a four-dimensional CA, s3
2 =
x1 x2 x3 x4
x1 Ω Ø Ø Ø
x2 Ø Ø Ω Ø
x3 Ø Ω Ø Ø
x4 Ø Ø Ø Ω
Axioms for Cylindrification
• Identity– cκ 0 = 0
• Order– F + cκ F = cκ F
• Semi-Distributive– cκ (F + cκ G) = cκ F + cκ G
• Commutative– cκcλ F = cλcκ F
Instantiation
• Take an n-ary relation, F = F(x1,…,xn)
• Fix xi = a, that is, consider the n-1-ary relation F|xi=a = F(x1,…,xi-1,a,xi+1,…,xn)
• Each column in the matrix of F|xi=a is:
Fxj|xi=a(v1,…,vn-2) =
F(v1,…,vj-1,xj,vj,…,vi-1,a,vi+1,…,vn)
Cylindrification as Union
• Cylindrification affects all slices in every non-maximal column
• Each slice in F|xi is a union of slices from
instantiations:Fxj|xi
(v1,…,vn-2) = U Fxj|xi=a(v1,…,vn-2)
aΩ
• Component-wise operation
Conclusion
• When cylindrification is defined as union of instantiations -
• Matrix representations of relations form a cylindrical algebra.