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Jonathan Leto

# NAME

Kleene's Algorithm - the theory behind it

brief introduction

# DESCRIPTION

## Semi-Rings

A Semi-Ring (S, +, ., 0, 1) is characterized by the following properties:

1)

a) `(S, +, 0) is a Semi-Group with neutral element 0`

b) `(S, ., 1) is a Semi-Group with neutral element 1`

c) `0 . a = a . 0 = 0 for all a in S`

2)

`"+"` is commutative and idempotent, i.e., `a + a = a`

3)

Distributivity holds, i.e.,

a) `a . ( b + c ) = a . b + a . c for all a,b,c in S`

b) `( a + b ) . c = a . c + b . c for all a,b,c in S`

4)

`SUM_{i=0}^{+infinity} ( a[i] )`

exists, is well-defined and unique

`for all a[i] in S`

and associativity, commutativity and idempotency hold

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] ) )``````

EXAMPLES:

• `S1 = ({0,1}, |, &, 0, 1)`

Boolean Algebra

• `S2 = (pos. reals with 0 and +infty, min, +, +infty, 0)`

Positive real numbers including zero and plus infinity

• `S3 = (Pot(Sigma*), union, concat, {}, {''})`

Formal languages over Sigma (= alphabet)

## 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.

## 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]``````

## Kleene's Algorithm and Semi-Rings

Kleene's algorithm can be applied to any Semi-Ring having the properties listed previously (above). (!)

EXAMPLES:

• `S1 = ({0,1}, |, &, 0, 1)`

`G(V,E)` be a graph with set of vertices V and set of edges E:

`m(v[i],v[j]) := ( (v[i],v[j]) in E ) ? 1 : 0`

Kleene's algorithm then calculates

`c^{n}_{i,j} = ( path from v[i] to v[j] exists ) ? 1 : 0`

using

`C^k[i,j] = C^k-1[i,j] | C^k-1[i,k] & C^k-1[k,j]`

(remember ` 0* = 1* = 1 `)

• `S2 = (pos. reals with 0 and +infty, min, +, +infty, 0)`

`G(V,E)` be a graph with set of vertices V and set of edges E, with costs `m(v[i],v[j])` associated with each edge `(v[i],v[j])` in E:

`m(v[i],v[j]) := costs of (v[i],v[j])`

`for all (v[i],v[j]) in E`

Set `m(v[i],v[j]) := +infinity` if an edge (v[i],v[j]) is not in E.

` ==> a* = 0 for all a in S2`

` ==> C^k[i,j] = min( C^k-1[i,j] ,`

` C^k-1[i,k] + C^k-1[k,j] )`

Kleene's algorithm then calculates the costs of the "shortest" path from any `v[i]` to any other `v[j]`:

`C^n[i,j] = costs of "shortest" path from v[i] to v[j]`

• `S3 = (Pot(Sigma*), union, concat, {}, {''})`

`M in DFA(Sigma)` be a Deterministic Finite Automaton with a set of states `Q`, a subset `F` of `Q` of accepting states and a transition function `delta : Q x Sigma --> Q`.

Define

`m(v[i],v[j]) :=`

` { a in Sigma | delta( q[i] , a ) = q[j] }`

and

`C^0[i,j] := m(v[i],v[j]);`

`if (i = j) then C^0[i,j] := C^0[i,j] union {''}`

(`{''}` is the set containing the empty string, whereas `{}` is the empty set!)

Then Kleene's algorithm calculates the language accepted by Deterministic Finite Automaton M using

`C^k[i,j] = C^k-1[i,j] union`

` C^k-1[i,k] concat ( C^k-1[k,k] )* concat C^k-1[k,j]`

and

`L(M) = UNION_{ q[j] in F } C^n[1,j]`

(state `q[1]` is assumed to be the "start" state)

finally being the language recognized by Deterministic Finite Automaton M.

Note that instead of using Kleene's algorithm, you can also use the "*" operator on the associated matrix:

Define `A[i,j] := m(v[i],v[j])`

` ==> A*[i,j] = c(v[i],v[j])`

Proof:

`A* = SUM_{i=0}^{+infty} A^i`

where `A^0 = E_{n}`

(matrix with one's in its main diagonal and zero's elsewhere)

and `A^(i+1) = A . A^i`

Induction over k yields:

`A^k[i,j] = c_{k}(v[i],v[j])`

`k = 0:`

`c_{0}(v[i],v[j]) = d_{i,j}`

with `d_{i,j} := (i = j) ? 1 : 0`

and `A^0 = E_{n} = [d_{i,j}]`

`k-1 -> k:`

`c_{k}(v[i],v[j])`

`= SUM_{l=1}^{n} m(v[i],v[l]) . c_{k-1}(v[l],v[j])`

`= SUM_{l=1}^{n} ( a[i,l] . a[l,j] )`

`= [a^{k}_{i,j}] = A^1 . A^(k-1) = A^k`

qed

In other words, the complexity of calculating the closure and doing matrix multiplications is of the same order `O( n^3 )` in Semi-Rings!