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Introduction to Algorithms

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KMP Algorithm KMP algorithm of T: abcac S: a b a b c a b c a c b a b T: a b c i j S: a b a b c a b c a c b a b T: a b c a c i j S: a b a b c a b c ... – PowerPoint PPT presentation

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Title: Introduction to Algorithms


1
Introduction to Algorithms
  • Jiafen Liu

Sept. 2013
2
Todays Tasks
  • Dynamic Programming
  • Longest common subsequence
  • Optimal substructure
  • Overlapping subproblems

3
Dynamic Programming
  • Programming often refer to Computer
    Programming.
  • But its not always the case, such as Linear
    Programming, Dynamic Programming.
  • Programming here means Design technique, it's a
    way of solving a class of problems, like
    divide-and-conquer.

4
Example LCS
  • Longest Common Subsequence (LCS)
  • Which is a problem that comes up in a variety of
    contextsPattern Recognition in Graphics,
    Revolution Tree in Biology, etc.
  • Given two sequences x1 . . mand y1 . . n,
    find a longest subsequence common to them both.
  • Why we address a but not the?
  • Usually the longest comment subsequence isn't
    unique.

5
Sequence Pattern Matching
  • Find the first appearance of string T in string
    S.
  • S a b a b c a b c a c b a b
  • T a b c a c
  • BF Thought
  • Pointer i and j to indicate current letter in S
    and T
  • When mismatching, j always backstep to 1
  • How about i?
  • i-j2

6
Sequence Matching Function
  • int Index(String S,String T, int pos)
  • ipos j1
  • while(ilt length(S) jlt length(T))
  • if(SiTj)ij
  • else iij-2j1
  • if(jgtlength(T))
  • return (i-length(T))
  • else return 0

7
Analysis of BF Algorithm
  • Whats the worst time complexity of BF algorithm
    for string S and T of length m and n ?
  • Thought of case
  • S00000000000000000000000001
  • T0000001
  • How to improve it?
  • KMP Algorithm

8
KMP algorithm of T abcac
  • S a b a b c a b c a c b a b
  • T a b c
  • S a b a b c a b c a c b a b
  • T a b c a c
  • S a b a b c a b c a c b a b
  • T (a)b c a c

9
How to do it?
  • When mismatching, i does not backstep, j backstep
    to some place particular to structure of string
    T.
  • T a b a a b c a c
  • nextj 0 1 1 2 2 3 1 2
  • T a a b a a d a a b a a b
  • nextj 0 1 2 1 2 3 1 2 3 4 5 6
  • How to get nextj given a string T?

10
How to do it?
  • When mismatching, i does not backstep, j backstep
    to some place particular to structure of string
    T.

11
Get nextj
  • Next10
  • Suppose nextjk, t1t2tk-1 tj-k1tj-k2tj-1
    nextj1?
  • if tktj, nextj1nextj1
  • Else treat it as a sequence matching of T to T
    itself, we have t1t2tk-1 tj-k1tj-k2tj-1 and
    tk?tj ,so we should compare tnextk and tj. If
    they are equal, nextj1nextk1, else
    backstep to nextnextk, and so on.

12
Implementation of nextj
  • void next(string T, int next)
  • i1j0 next10
  • while (iltlength(T))
  • if(j0 or TiTj)
  • ijnextij
  • else jnextj
  • Analysis of KMP

13
Longest Common Subsequence
  • x A B C B D A B
  • y B D C A B A
  • What is a longest common subsequence?
  • BD?
  • Extend the notation of subsequence. Of the same
    order, but not necessarily successive.
  • BDA?BDB?BCBA?BCAB?
  • Is there any of length 5?
  • We can mark BCBA and BCAB with functional
    notation LCS(x,y), but its not a function.

14
Brute-force LCS algorithm
  • How to find a LCS?
  • Check every subsequence of x1 . . mto see if it
    is also a subsequence of y1 . . n.
  • Analysis
  • Given a subsequence of x, such as BCBA , How long
    does it take to check whether it's a subsequence
    of y?
  • O(n) time per subsequence.

15
Analysis of brute LCS
  • Analysis
  • How many subsequences of x are there?
  • 2m subsequences of x.
  • Because each bit-vector of length m determines a
    distinct subsequence of x.
  • So, worst-case running time is ?
  • O(n2m),which is an exponential time.

16
Towards a better algorithm
  • Simplification
  • Look at the length of a longest-common
    subsequence.
  • Extend the algorithm to find the LCS itself.
  • Now we just focus on the problem of computing the
    length.
  • Notation Denote the length of a sequence s by
    s.
  • We want to compute is LCS(x,y) . How can we do
    it?

17
Towards a better algorithm
  • Strategy Consider prefixes of x and y.
  • Define ci, j LCS(x1 . . i, y1 . . j).
    And we will calculate ci,j for all i and j.
  • If we reach there, how can we solve the problem
    of LCS(x, y)?
  • Simple, LCS(x, y)cm,n

18
Towards a better algorithm
  • Theorem.
  • Thats what we are going to prove.
  • Proof.
  • Lets start with case xi yj. Try it.

19
Towards a better algorithm
  • Suppose ci, j k, and let z1 . . k LCS(x1
    . . i, y1 . . j). what zk here is?
  • Then, zk xi ( yj), why?
  • Or else z could be extended by tacking on xi
    and yj.
  • Thus, z1 . . k1 is CS of x1 . . i1 and y1
    . . j1. Its obvious to us.

20
A Claim easy to prove
  • Claim z1 . . k1 LCS(x1 . . i1, y1 . .
    j1).
  • Suppose w is a longer CS of x1 . . i1 and y1
    . . j1.
  • That means w gt k1.
  • Then, cut and paste wzk is a common
    subsequence of x1 . . i and y1 . . j with
    wzk gt k.
  • Contradiction, proving the claim.

21
Towards a better algorithm
  • Thus, ci1, j1 k1, which implies that ci,
    j ci1, j1 1.
  • The other case is similar. Prove by yourself.
  • Hints
  • if zk xi, then zk ? yj,
    ci,jci,j-1
  • else if zk yj, then zk ? xi,
    ci,jci-1,j
  • else ci,jci,j-1 ci-1,j

22
Dynamic-programming hallmark
  • Dynamic-programming hallmark 1

23
Optimal substructure
  • In problem of LCS, the base idea
  • If z LCS(x, y), then any prefix of z is an LCS
    of a prefix of x and a prefix of y.
  • If the substructure were not optimal, then we can
    find a better solution to the overall problem
    using cut and paste.

24
Recursive algorithm for LCS
  • LCS(x, y, i, j) //ignoring base case
  • if xi y j
  • then ci, j ?LCS(x, y, i1, j1) 1
  • else ci, j ?max LCS(x, y, i1, j) ,
  • LCS(x, y, i, j1)
  • return ci,j
  • What's the worst case for this program?
  • Which of these two clauses is going to cause us
    more headache?
  • Why?

25
the worst case of LCS
  • The worst case is xi ? y j for all i and j
  • In which case, the algorithm evaluates two sub
    problems, each with only one parameter
    decremented.
  • We are going to generate a tree.

26
Recursion tree
  • m3, n4

27
Recursion tree
  • m3, n4

28
Recursion tree
  • m3, n4

29
Recursion tree
  • What is the height of this tree?
  • max(m,n)?
  • mn , That means work exponential.

30
Recursion tree
  • Have you observed something interesting about
    this tree?
  • There's a lot of repeated work. The same subtree,
    the same subproblem that you are solving.

31
Repeated work
  • When you find you are repeating something,
    figure out a way of not doing it.
  • That brings up our second hallmark for dynamic
    programming.

32
Dynamic-programming hallmark
  • Dynamic-programming hallmark 2

33
Overlapping subproblems
  • The number of nodes indicates the number of
    subproblems. What is the size of the former tree?
  • 2mn
  • What is the number of distinct LCS subproblems
    for two strings of lengths m and n?
  • mn.
  • How to solve overlapping subproblems?

34
Memoization
  • Memoization After computing a solution to a
    subproblem, store it in a table. Subsequent calls
    check the table to avoid redoing work.
  • Here is the improved algorithm of LCS. And the
    basic idea is keeping a table of ci,j.

35
Improved algorithm of LCS
  • LCS(x, y, i, j) //ignoring base case
  • if ci, j NIL
  • then if xi y j
  • then ci, j ?LCS(x, y, i1, j1) 1
  • else ci, j ?max LCS(x, y, i1, j) ,
  • LCS(x, y, i, j1)
  • return ci, j
  • How much time does it take to execute?

Same as before
36
Analysis
  • Time T(mn), why?
  • Because every cell only costs us a constant
    amount of time .
  • Constant work per table entry, so the total is
    T(mn)
  • How much space does it take?
  • Space T(mn).

37
Dynamic programming
  • Memoization is a really good strategy in
    programming for many things where, when you have
    the same parameters, you're going to get the same
    results.
  • Another strategy for doing exactly the same
    calculation is in a bottom-up way.
  • IDEA of LCS make a ci,j table and find an
    orderly way of filling in the table compute the
    table bottom-up.

38
Dynamic-programming algorithm
  • A B C B D A B
  • B
  • D
  • C
  • A
  • B
  • A

0 0 0 0 0 0 0 0
0
0
0
0
0
0
39
Dynamic-programming algorithm

  • Time ?

  • T(mn)

40
Dynamic-programming algorithm
  • How to find the LCS ?

41
Dynamic-programming algorithm
  • Reconstruct LCS by tracing backwards.

42
Dynamic-programming algorithm
  • And this is just one path back. We could have a
    different LCS.

43
Cost of LCS
  • Time T(mn).
  • Space T(mn).
  • Think about that
  • Can we use space of T(minm,n)?
  • In fact, We don't need the whole table.
  • We could do it either running vertically or
    running horizontally, whichever one gives us the
    smaller space.

44
Further thought
  • But we can not go backwards then because we've
    lost the information in front rows.
  • HINT Divide and Conquer.

45
Have FUN !
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