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=head1 NAME
Kleene's Algorithm - the theory behind it
brief introduction
=head1 DESCRIPTION
=head2 B<Semi-Rings>
A Semi-Ring (S, +, ., 0, 1) is characterized by the following properties:
=over 4
=item 1)
a) C<(S, +, 0) is a Semi-Group with neutral element 0>
b) C<(S, ., 1) is a Semi-Group with neutral element 1>
c) C<0 . a = a . 0 = 0 for all a in S>
=item 2)
C<"+"> is commutative and B<idempotent>, i.e., C<a + a = a>
=item 3)
Distributivity holds, i.e.,
a) C<a . ( b + c ) = a . b + a . c for all a,b,c in S>
b) C<( a + b ) . c = a . c + b . c for all a,b,c in S>
=item 4)
C<SUM_{i=0}^{+infinity} ( a[i] )>
exists, is well-defined and unique
C<for all a[i] in S>
and associativity, commutativity and idempotency hold
=item 5)
Distributivity for infinite series also holds, i.e.,
( SUM_{i=0}^{+infty} a[i] ) . ( SUM_{j=0}^{+infty} b[j] )
= SUM_{i=0}^{+infty} ( SUM_{j=0}^{+infty} ( a[i] . b[j] ) )
=back
EXAMPLES:
=over 4
=item *
C<S1 = ({0,1}, |, &, 0, 1)>
Boolean Algebra
See also L<Math::MatrixBool(3)>
=item *
C<S2 = (pos. reals with 0 and +infty, min, +, +infty, 0)>
Positive real numbers including zero and plus infinity
See also L<Math::MatrixReal(3)>
=item *
C<S3 = (Pot(Sigma*), union, concat, {}, {''})>
Formal languages over Sigma (= alphabet)
See also L<DFA::Kleene(3)>
=back
=head2 B<Operator '*'>
(reflexive and transitive closure)
Define an operator called "*" as follows:
a in S ==> a* := SUM_{i=0}^{+infty} a^i
where
a^0 = 1, a^(i+1) = a . a^i
Then, also
a* = 1 + a . a*, 0* = 1* = 1
hold.
=head2 B<Kleene's Algorithm>
In its general form, Kleene's algorithm goes as follows:
for i := 1 to n do
for j := 1 to n do
begin
C^0[i,j] := m(v[i],v[j]);
if (i = j) then C^0[i,j] := C^0[i,j] + 1
end
for k := 1 to n do
for i := 1 to n do
for j := 1 to n do
C^k[i,j] := C^k-1[i,j] +
C^k-1[i,k] . ( C^k-1[k,k] )* . C^k-1[k,j]
for i := 1 to n do
for j := 1 to n do
c(v[i],v[j]) := C^n[i,j]
=head2 B<Kleene's Algorithm and Semi-Rings>
Kleene's algorithm can be applied to any Semi-Ring having the properties
listed previously (above). (!)
EXAMPLES:
=over 4
=item *
C<S1 = ({0,1}, |, &, 0, 1)>
C<G(V,E)> be a graph with set of vertices V and set of edges E:
C<m(v[i],v[j]) := ( (v[i],v[j]) in E ) ? 1 : 0>
Kleene's algorithm then calculates
C<c^{n}_{i,j} = ( path from v[i] to v[j] exists ) ? 1 : 0>
using
C<C^k[i,j] = C^k-1[i,j] | C^k-1[i,k] & C^k-1[k,j]>
(remember C< 0* = 1* = 1 >)
=item *
C<S2 = (pos. reals with 0 and +infty, min, +, +infty, 0)>
C<G(V,E)> be a graph with set of vertices V and set of edges E, with
costs C<m(v[i],v[j])> associated with each edge C<(v[i],v[j])> in E:
C<m(v[i],v[j]) := costs of (v[i],v[j])>
C<for all (v[i],v[j]) in E>
Set C<m(v[i],v[j]) := +infinity> if an edge (v[i],v[j]) is not in E.
C< ==E<gt> a* = 0 for all a in S2>
C< ==E<gt> C^k[i,j] = min( C^k-1[i,j] ,>
C< C^k-1[i,k] + C^k-1[k,j] )>
Kleene's algorithm then calculates the costs of the "shortest" path
from any C<v[i]> to any other C<v[j]>:
C<C^n[i,j] = costs of "shortest" path from v[i] to v[j]>
=item *
C<S3 = (Pot(Sigma*), union, concat, {}, {''})>
C<M in DFA(Sigma)> be a Deterministic Finite Automaton with a set of
states C<Q>, a subset C<F> of C<Q> of accepting states and a transition
function C<delta : Q x Sigma --E<gt> Q>.
Define
C<m(v[i],v[j]) :=>
C< { a in Sigma | delta( q[i] , a ) = q[j] }>
and
C<C^0[i,j] := m(v[i],v[j]);>
C<if (i = j) then C^0[i,j] := C^0[i,j] union {''}>
(C<{''}> is the set containing the empty string, whereas C<{}> is the
empty set!)
Then Kleene's algorithm calculates the language accepted by Deterministic
Finite Automaton M using
C<C^k[i,j] = C^k-1[i,j] union>
C< C^k-1[i,k] concat ( C^k-1[k,k] )* concat C^k-1[k,j]>
and
C<L(M) = UNION_{ q[j] in F } C^n[1,j]>
(state C<q[1]> is assumed to be the "start" state)
finally being the language recognized by Deterministic Finite Automaton M.
=back
Note that instead of using Kleene's algorithm, you can also use the "*"
operator on the associated matrix:
Define C<A[i,j] := m(v[i],v[j])>
C< ==E<gt> A*[i,j] = c(v[i],v[j])>
Proof:
C<A* = SUM_{i=0}^{+infty} A^i>
where C<A^0 = E_{n}>
(matrix with one's in its main diagonal and zero's elsewhere)
and C<A^(i+1) = A . A^i>
Induction over k yields:
C<A^k[i,j] = c_{k}(v[i],v[j])>
=over 10
=item C<k = 0:>
C<c_{0}(v[i],v[j]) = d_{i,j}>
with C<d_{i,j} := (i = j) ? 1 : 0>
and C<A^0 = E_{n} = [d_{i,j}]>
=item C<k-1 -E<gt> k:>
C<c_{k}(v[i],v[j])>
C<= SUM_{l=1}^{n} m(v[i],v[l]) . c_{k-1}(v[l],v[j])>
C<= SUM_{l=1}^{n} ( a[i,l] . a[l,j] )>
C<= [a^{k}_{i,j}] = A^1 . A^(k-1) = A^k>
=back
qed
In other words, the complexity of calculating the closure and doing
matrix multiplications is of the same order C<S<O( n^3 )>> in Semi-Rings!
=head1 SEE ALSO
Math::MatrixBool(3), Math::MatrixReal(3), DFA::Kleene(3).
(All contained in the distribution of the "Set::IntegerFast" module)
Dijkstra's algorithm for shortest paths.
=head1 AUTHOR
This document is based on lecture notes and has been put into
POD format by Steffen Beyer <sb@engelschall.com>.
=head1 COPYRIGHT
Copyright (c) 1997 by Steffen Beyer. All rights reserved.
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