Presentation is loading. Please wait.

Presentation is loading. Please wait.

CSE 421 Algorithms Richard Anderson Lecture 19 Longest Common Subsequence.

Similar presentations


Presentation on theme: "CSE 421 Algorithms Richard Anderson Lecture 19 Longest Common Subsequence."— Presentation transcript:

1 CSE 421 Algorithms Richard Anderson Lecture 19 Longest Common Subsequence

2 C=c 1 …c g is a subsequence of A=a 1 …a m if C can be obtained by removing elements from A (but retaining order) LCS(A, B): A maximum length sequence that is a subsequence of both A and B ocurranec occurrence attacggct tacgacca Instructor Example

3 Determine the LCS of the following strings Student Submission BARTHOLEMEWSIMPSON KRUSTYTHECLOWN

4 String Alignment Problem Align sequences with gaps Charge  x if character x is unmatched Charge  xy if character x is matched to character y CAT TGA AT CAGAT AGGA

5 LCS Optimization A = a 1 a 2 …a m B = b 1 b 2 …b n Opt[j, k] is the length of LCS(a 1 a 2 …a j, b 1 b 2 …b k )

6 Optimization recurrence If a j = b k, Opt[j,k] = 1 + Opt[j-1, k-1] If a j != b k, Opt[j,k] = max(Opt[j-1,k], Opt[j,k-1])

7 Give the Optimization Recurrence for the String Alignment Problem Charge  x if character x is unmatched Charge  xy if character x is matched to character y Student Submission

8 Dynamic Programming Computation

9 Write the code to compute Opt[j,k] Student Submission

10 Storing the path information A[1..m], B[1..n] for i := 1 to m Opt[i, 0] := 0; for j := 1 to n Opt[0,j] := 0; Opt[0,0] := 0; for i := 1 to m for j := 1 to n if A[i] = B[j] { Opt[i,j] := 1 + Opt[i-1,j-1]; Best[i,j] := Diag; } else if Opt[i-1, j] >= Opt[i, j-1] { Opt[i, j] := Opt[i-1, j], Best[i,j] := Left; } else { Opt[i, j] := Opt[i, j-1], Best[i,j] := Down; } a 1 …a m b 1 …b n

11 How good is this algorithm? Is it feasible to compute the LCS of two strings of length 100,000 on a standard desktop PC? Why or why not. Student Submission

12 Observations about the Algorithm The computation can be done in O(m+n) space if we only need one column of the Opt values or Best Values The algorithm can be run from either end of the strings

13 Divide and Conquer Algorithm Where does the best path cross the middle column? For a fixed i, and for each j, compute the LCS that has a i matched with b j

14 Constrained LCS LCS i,j (A,B): The LCS such that –a 1,…,a i paired with elements of b 1,…,b j –a i+1,…a m paired with elements of b j+1,…,b n LCS 4,3 (abbacbb, cbbaa)

15 A = RRSSRTTRTS B=RTSRRSTST Compute LCS 5,1 (A,B), LCS 5,2 (A,B),…,LCS 5,9 (A,B) Student Submission

16 A = RRSSRTTRTS B=RTSRRSTST Compute LCS 5,1 (A,B), LCS 5,2 (A,B),…,LCS 5,9 (A,B) Instructor Example jleftright 003 113 213 323 433 532 632 731 841 940

17 Computing the middle column From the left, compute LCS(a 1 …a m/2,b 1 …b j ) From the right, compute LCS(a m/2+1 …a m,b j+1 …b n ) Add values for corresponding j’s Note – this is space efficient

18 Divide and Conquer A = a 1,…,a m B = b 1,…,b n Find j such that –LCS(a 1 …a m/2, b 1 …b j ) and –LCS(a m/2+1 …a m,b j+1 …b n ) yield optimal solution Recurse

19 Algorithm Analysis T(m,n) = T(m/2, j) + T(m/2, n-j) + cnm

20 Prove by induction that T(m,n) <= 2cmn Instructor Example


Download ppt "CSE 421 Algorithms Richard Anderson Lecture 19 Longest Common Subsequence."

Similar presentations


Ads by Google