Dynamic programming longest common subsequence
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Dynamic Programming (Longest Common Subsequence). Subsequence. String Z is a subsequence of string X if Z’s characters appear in X following the same left-to-right order. X = < A, B, C, T, D, G, N, A, B > Z = < B, D, A >. Longest Common Subsequence (LCS).

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Dynamic Programming (Longest Common Subsequence)

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Dynamic programming longest common subsequence

Dynamic Programming(Longest Common Subsequence)


Subsequence

Subsequence

  • String Z is a subsequence of string X if Z’s characters appear in X following the same left-to-right order

X = < A, B, C, T, D, G, N, A, B >

Z = < B, D, A >


Longest common subsequence lcs

Longest Common Subsequence (LCS)

  • String Z is a common subsequence of strings X and Y if Z’s characters appear in both X & Y following the same left-to-right order

X = < A, B, C, T, B, D, A, B >

Y = < B, D, C, A, B, A >

  • < B, C, A > is a common subsequence of both X and Y.

  • < B, C, B, A > or < B, C, A, B > is the Longest Common Subsequence (LCS) of X and Y.

LCS is used to measure the similarity between two strings X and Y. The longer the LCS , the more similar X and Y


Lcs problem definition

LCS Problem Definition

  • We are given two sequences

    • X= <x1,x2,...,xm>,and

    • Y = <y1,y2,...,yn>

  • We need to find the LCS between X and Y

Very common in DNA sequences


Characterization of lcs

Characterization of LCS


Characterization of lcs cont d

Characterization of LCS (Cont’d)


Recursive nature of lcs

Recursive Nature of LCS

  • Implications of Theorem 15.1


Recursive equation

Recursive Equation

  • Input X = <x1, x2, …., xm>

    Y = <y1, y2, ………, yn>

  • Assume C[i, j] is the LCS for the first i positions in X with the first j positions in Y

    • C[i,j] = LCS(<x1, x2, …., xi>, <y1, y2, ………, yj>)

Our goal is to compute C[m,n]


Dynamic programming for lcs

Dynamic Programming for LCS

Initialization step


Dynamic programming for lcs1

Dynamic Programming for LCS

If matching, go diagonal


Dynamic programming for lcs2

Dynamic Programming for LCS

Else select the larger of top or left


Dynamic programming for lcs3

Dynamic Programming for LCS

Note that array c  keeps track of the cost,

Array b  keeps track of the parent (to backtrack)


Summary of the main strategies

Summary of the Main Strategies


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