1. Chapter 3
String Matching

2. String Matching
Given: Two strings T[1..n] and P[1..m] over alphabet .
Want to find all occurrences of P[1..m] “the pattern” in T[1..n] “the text”.
Example:  = {a, b, c}
Text T
pattern P a b c
- P occurs with shift s.
- P occurs beginning at position s+1.
s is a valid shift.
The idea of the string matching problem is that we want to find all occurrences of the pattern P in the given text T
s=3 a b

3. Sequential Search

4. Naïve String Matching Using Brute Force Technique

5. Naïve String Matching method
n ≡ size of input string
m ≡ size of pattern to be matched
O( (n-m+1)m )
Θ( n2 ) if m = floor( n/2 )
We can do better

6. Rabin Karp String Matching
Consider a hashing scheme
Let characters in both arrays T and P be digits in radix-S notation. (S = (0,1,...,9)
Assume each character is digit in radix-d notation (e.g. d=10)
Let p be the value of the characters in P
Choose a prime number q such that fits within a computer word to speed computations.
Compute (p mod q)
The value of p mod q is what we will be using to find all matches of the pattern P in T.

7. Compute (T[s+1, .., s+m] mod q) for s = 0 .. n-m
Test against P only those sequences in T having the same (mod q) value

9. Assume each character is digit in radix-d notation (e.g. d=10)
p = decimal value of pattern
ts = decimal value of substring T[s+1..s+m] for s = 0,1...,n-m
s = a valid shift
We never explicitly compute a new value.
We simply adjust the existing value as we move over one character.

11. Performance of Robin Karp:-
Preprocessing (determining each pattern hash)
Θ( m )
Worst case running time
Θ( (n-m+1)m )
No better than naïve method
Expected case
If we assume the number of hits is constant compared to n, we expect O( n )
Only pattern-match “hits” – not all shifts

12. The Knuth-Morris-Pratt Algorithm
Knuth, Morris and Pratt proposed a linear time algorithm for the string matching problem.
A matching time of O(n) is achieved by avoiding comparisons with elements of ‘S’ that have previously been involved in comparison with some element of the pattern ‘p’ to be matched. i.e., backtracking on the string ‘S’ never occurs

13. Components of KMP algorithm
The prefix function, Π
The prefix function,Π for a pattern encapsulates knowledge about how the pattern matches against shifts of itself. This information can be used to avoid useless shifts of the pattern ‘p’. In other words, this enables avoiding backtracking on the string ‘S’.
The KMP Matcher
With string ‘S’, pattern ‘p’ and prefix function ‘Π’ as inputs, finds the occurrence of ‘p’ in ‘S’ and returns the number of shifts of ‘p’ after which occurrence is found.

14. The prefix function, Π
Following pseudocode computes the prefix fucnction, Π:
Compute-Prefix-Function (p)
1 m  length[p] //’p’ pattern to be matched
2 Π[1]  0
3 k  0
for q  2 to m
do while k > 0 and p[k+1] != p[q]
do k  Π[k]
If p[k+1] = p[q]
then k  k +1
Π[q]  k
return Π

15. Example: compute Π for the pattern ‘p’ below: p a b c
Initially: m = length[p] = 7
Π[1] = 0
k = 0
Step 1: q = 2, k=0
Π[2] = 0
Step 2: q = 3, k = 0,
Π[3] = 1
Step 3: q = 4, k = 1
Π[4] = 2 q 1 2 3 4 5 6 7 p a b c Π q 1 2 3 4 5 6 7 p a b c Π q 1 2 3 4 5 6 7 p a b c A Π

16. Step 4: q = 5, k =2 Π[5] = 3 Step 5: q = 6, k = 3 Π[6] = 0
Π[7] = 1
After iterating 6 times, the prefix function computation is complete:  q 1 2 3 4 5 6 7 p a b c Π q 1 2 3 4 5 6 7 p a b c Π q 1 2 3 4 5 6 7 p a b c Π q 1 2 3 4 5 6 7 p a b A c Π

17. The KMP Matcher
The KMP Matcher, with pattern ‘p’, string ‘S’ and prefix function ‘Π’ as input, finds a match of p in S.
Following pseudocode computes the matching component of KMP algorithm:
KMP-Matcher(S,p)
1 n  length[S]
2 m  length[p]
3 Π  Compute-Prefix-Function(p)
4 q  //number of characters matched
5 for i  1 to n //scan S from left to right
do while q > 0 and p[q+1] != S[i]
do q  Π[q] //next character does not match
if p[q+1] = S[i]
then q  q //next character matches
if q = m //is all of p matched?
then print “Pattern occurs with shift” i – m
q  Π[ q] // look for the next match
Note: KMP finds every occurrence of a ‘p’ in ‘S’. That is why KMP does not terminate in step 12, rather it searches remainder of ‘S’ for any more occurrences of ‘p’.

18. Illustration: given a String ‘S’ and pattern ‘p’ as follows: S b a c
Let us execute the KMP algorithm to find whether ‘p’ occurs in ‘S’.
For ‘p’ the prefix function, Π was computed previously and is as follows: q 1 2 3 4 5 6 7 p a b A c Π

19. S b a c p a b c S b a c p a b c Initially: n = size of S = 15;
m = size of p = 7
Step 1: i = 1, q = 0
comparing p[1] with S[1] S b a c p a b c
P[1] does not match with S[1]. ‘p’ will be shifted one position to the right.
Step 2: i = 2, q = 0
comparing p[1] with S[2] S b a c p a b c
P[1] matches S[2]. Since there is a match, p is not shifted.

20. S b a c p a b c S b a c p a b c b a c S p a b c Step 3: i = 3, q = 1
Comparing p[2] with S[3]
p[2] does not match with S[3] S b a c p a b c
Backtracking on p, comparing p[1] and S[3]
Step 4: i = 4, q = 0
comparing p[1] with S[4]
p[1] does not match with S[4] S b a c p a b c
Step 5: i = 5, q = 0
comparing p[1] with S[5]
p[1] matches with S[5] b a c S p a b c

21. S b a c p a b c b a c S p a b c S b a c p a b c Step 6: i = 6, q = 1
Comparing p[2] with S[6]
p[2] matches with S[6] S b a c p a b c
Step 7: i = 7, q = 2
Comparing p[3] with S[7]
p[3] matches with S[7] b a c S p a b c
Step 8: i = 8, q = 3
Comparing p[4] with S[8]
p[4] matches with S[8] S b a c p a b c

22. S b a c p a b c b a c S p a b c b a c S p a b c Step 9: i = 9, q = 4
Comparing p[5] with S[9]
p[5] matches with S[9] S b a c p a b c
Step 10: i = 10, q = 5
Comparing p[6] with S[10]
p[6] doesn’t match with S[10] b a c S p a b c
Backtracking on p, comparing p[4] with S[10] because after mismatch q = Π[5] = 3
Step 11: i = 11, q = 4
Comparing p[5] with S[11]
p[5] matches with S[11] b a c S p a b c

23. S b a c p a b c S b a c p a b c Step 12: i = 12, q = 5
Comparing p[6] with S[12]
p[6] matches with S[12] S b a c p a b c
Step 13: i = 13, q = 6
Comparing p[7] with S[13]
p[7] matches with S[13] S b a c p a b c
Pattern ‘p’ has been found to completely occur in string ‘S’. The total number of shifts
that took place for the match to be found are: i – m = 13 – 7 = 6 shifts.

24. Running - time analysis
Compute-Prefix-Function (Π)
1 m  length[p] //’p’ pattern to be matched
2 Π[1]  0
3 k  0
for q  2 to m
do while k > 0 and p[k+1] != p[q]
do k  Π[k]
If p[k+1] = p[q]
then k  k +1
Π[q]  k
return Π
In the above pseudocode for computing the prefix function, the for loop from step 4 to step 10 runs ‘m’ times. Step 1 to step 3 take constant time. Hence the running time of compute prefix function is Θ(m).
KMP Matcher
1 n  length[S]
2 m  length[p]
3 Π  Compute-Prefix-Function(p)
4 q  0
5 for i  1 to n
6 do while q > 0 and p[q+1] != S[i]
do q  Π[q]
if p[q+1] = S[i]
then q  q + 1
if q = m
then print “Pattern occurs with shift” i – m
q  Π[ q]
The for loop beginning in step 5 runs ‘n’ times, i.e., as long as the length of the string ‘S’. Since step 1 to step 4 take constant time, the running time is dominated by this for loop. Thus running time of matching function is Θ(n).

25. Closest-Pair Problem
Find the two closest points in a set of n points (in the two-dimensional Cartesian plane).
Brute-force algorithm
Compute the distance between every pair of distinct points
and return the indexes of the points for which the distance is the smallest.
Draw example.

26. Closest-Pair Brute-Force Algorithm (cont.)
The basic operation of the algorithm is computing the Euclidean distance between two points.
The square root is a complex operation who’s result is often irrational, therefore the results
can be found only approximately. Computing such operations are not trivial. One can avoid
computing square roots by comparing distance squares instead.
Efficiency:
How to make it faster?
Θ(n^2) multiplications (or sqrt)
Using divide-and-conquer!

27. Brute-Force Strengths and Weaknesses
wide applicability
simplicity
yields reasonable algorithms for some important problems (e.g., matrix multiplication, sorting, searching, string matching)
Weaknesses
rarely yields efficient algorithms
some brute-force algorithms are unacceptably slow
not as constructive as some other design techniques

28. Convex Hull

32. For Finding Extreme Points Simplex Method can be used from LPP.

33. Exhaustive Search
A brute force solution to a problem involving search for an element with a special property, usually among combinatorial objects such as permutations, combinations, or subsets of a set.
Method:
generate a list of all potential solutions to the problem in a systematic manner .
evaluate potential solutions one by one, disqualifying infeasible ones and, for an optimization problem, keeping track of the best one found so far
when search ends, announce the solution(s) found

34. Example 1: Traveling Salesman Problem
Given n cities with known distances between each pair, find the shortest tour that passes through all the cities exactly once before returning to the starting city
Alternatively: Find shortest Hamiltonian circuit in a weighted connected graph
Example: a b c d 8 2 7 5 3 4
How do we represent a solution (Hamiltonian circuit)?

35. TSP by Exhaustive Search
Tour Cost
a→b→c→d→a = 17
a→b→d→c→a = 21
a→c→b→d→a = 20
a→c→d→b→a = 21
a→d→b→c→a = 20
a→d→c→b→a = 17
Efficiency:
Θ((n-1)!)

36. Example 2: Knapsack Problem
Given n items:
weights: w1 w2 … wn
values: v1 v2 … vn
a knapsack of capacity W
Find most valuable subset of the items that fit into the knapsack
Example: Knapsack capacity W=16
item weight value
$20
$30
$50
$10

37. Knapsack Problem by Exhaustive Search
Subset Total weight Total value
{1} $20
{2} $30
{3} $50
{4} $10
{1,2} $50
{1,3} $70
{1,4} $30
{2,3} $80
{2,4} $40
{3,4} $60
{1,2,3} not feasible
{1,2,4} $60
{1,3,4} not feasible
{2,3,4} not feasible
{1,2,3,4} not feasible
Efficiency:
Θ(2^n)

38. Example 3: The Assignment Problem
There are n people who need to be assigned to n jobs, one person per job. The cost of assigning person i to job j is C[i,j]. Find an assignment that minimizes the total cost.
Job 0 Job 1 Job 2 Job 3
Person
Person
Person
Person
Algorithmic Plan: Generate all legitimate assignments, compute their costs, and select the cheapest one.
How many assignments are there?
Pose the problem as one about a cost matrix: n!
cycle cover in a graph

39. Assignment Problem by Exhaustive Search
Assignment (col.#s) Total Cost
1, 2, 3, =18
1, 2, 4, =30
1, 3, 2, =24
1, 3, 4, =26
1, 4, 2, =33
1, 4, 3, =23
etc.
(For this particular instance, the optimal assignment can be found by exploiting the specific features of the number given. It is: )
C =
2,1,3,4

40. Final Comments on Exhaustive Search
Exhaustive-search algorithms run in a realistic amount of time only on very small instances
In some cases, there are much better alternatives!
Euler circuits
shortest paths
minimum spanning tree
assignment problem
In many cases, exhaustive search or its variation is the only known way to get exact solution

41. Voronoi Diagram
The partitioning of a plane with points into convex polygons such that each polygon contains exactly one generating point and every point in a given polygon is closer to its generating point than to any other. A Voronoi diagram is sometimes also known as a Dirichlet tessellation. The cells are called Dirichlet regions, Thiessen polytopes, or Voronoi polygons.

42. History of Voronoi Diagram
Voronoi diagrams were considered as early at 1644 by René Descartes and were used by Dirichlet (1850) in the investigation of positive quadratic forms. They were also studied by Voronoi (1907), who extended the investigation of Voronoi diagrams to higher dimensions. They find widespread applications in areas such as computer graphics, epidemiology, geophysics, and meteorology. A particularly notable use of a Voronoi diagram was the analysis of the 1854 cholera epidemic in London, in which physician John Snow determined a strong correlation of deaths with proximity to a particular (and infected) water pump on Broad Street.

44. The Mathematica command VoronoiDiagram[pts] in the Mathematica package ComputationalGeometry` returns a data structure corresponding to the Voronoi diagram of a given set of points, and DiagramPlot[pts] gives a graphical illustration of the Voronoi diagram (left figure above). Voronoi diagrams can be even more easily visualized in Mathematica using graphics functions such as ListDensityPlot and ListPlot3D with the option setting InterpolationOrder -> 0 (right two figures).
The Delaunay triangulation and Voronoi diagram in are dual to each other in the graph theoretical sense.