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# Dynamic programming lcs

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### Dynamic programming lcs

1. 1. Longest Common Subsequence(LCS) 研究生 鍾聖彥 指導老師 許慶昇 Dynamic Programming 1 2014/05/07 最長共同子序列
2. 2. Dynamic Programming Optimal substructure(當一個問題存在著最 佳解，則表示其所有子問題也必存在著最佳解) Overlapping subproblems(子問題重複出 現) 2
3. 3. Longest Common Subsequence??? Biological applications often need to compare the DNA of tow(or more) different organisms. 3
4. 4. Subsequence A subsequence of a given sequence is just the given sequence with zero or more elements left out. Ex: app、le、ple and so on are subsequences of “apple”. 4
5. 5. Common Subsequence X = (A, B, C, B, D, A, B) Y = (B, D, C, A, B, A) Two sequences: Sequence Z is a common subsequence of X and Y if Z is a subsequence of both X and Y Z = (B, C, A) — length 3 Z = (B, C, A, B) - length 4 Z = (B, D, A, B) — length 4 Z= — length 5 ??? longest 5
6. 6. What is longest Common Subsequence problem? X = (x1, x2,……., xm) Y = (y1, y2,……., yn) 6 Find a maximum-length common subsequence of X and Y How to do? Dynamic Programming!!! Brute Force!!!
7. 7. Step 1: Characterize optimality Sequence X = (x1, x2,……., xm) Define the ith prefix of X, for i = 0, 1,…, m as Xi = (x1, x2, ..., xi) with X0 representing the empty sequence. EX: if X = (A, B, C, A, D, A, B) then X4 = (A, B, C, A) X0 = ( ) empty sequence 7
8. 8. Theorem (Optimal substructure of LCS) 8 1. If Xm = Yn, then Zk = Xm = Yn and Zk-1 is a LCS of Xm-1 and Yn-1 2. If Xm ≠ Yn, then Zk ≠ Xm implies that Z is a LCS of Xm-1 and Y 3. If Xm ≠ Yn, then Zk ≠ Yn implies that Z is a LCS of X and Yn-1 X = (X1, X2,…, Xm) and Y = (Y1, Y2,…, Yn) Sequences Z = (Z1, Z2,…, Zk) be any LCS of X and Y We assume:
9. 9. Optimal substructure problem The LCS of the original two sequences contains a LCS of prefixes of the two sequences. (當一個問題存在著最佳解，則表示其所有子問題也必存 在著最佳解) 9
10. 10. Step 2: A recursive solution Xi and Yj end with xi=yj Zk is Zk -1 followed by Zk = Xi = Yj where Zk-1 is an LCS of Xi-1 and Yj -1 LenLCS(i, j) = LenLCS(i-1, j-1)+1 Xi x1 x2 … xi-1 xi Yj y1 y2 … yj-1 yj=xi Zk z1 z2…zk-1 zk =yj=xi Case 1:
11. 11. Step 2: A recursive solution Case 2,3: Xi and Yj end with xi ≠ yj Xi x1 x2 … xi-1 xi Yj y1 y2 … yj-1 yj Zk z1 z2…zk-1 zk ≠yj Xi x1 x2 … xi-1 x i Yj yj y1 y2 …yj-1 yj Zk z1 z2…zk-1 zk ≠ xi Zk is an LCS of Xi and Yj -1 Zk is an LCS of Xi-1 and Yj LenLCS(i, j)=max{LenLCS(i, j-1), LenLCS(i-1, j)}
12. 12. Step 2:A recursive solution Let c[i,j] be the length of a LCS for Xi and Yj the recursion described by the above cases as 12 Case 1 Reduces to the single subproblem of finding a LCS of Xm-1, Yn-1 and adding Xm = Yn to the end of Z. Cases 2 and 3 Reduces to two subproblems of finding a LCS of Xm-1, Y and X, Yn-1 and selecting the longer of the two.
13. 13. Step 3: Compute the length of the LCS LCS problem has only ɵ(mn) distinct subproblems. So? Use Dynamic programming!!! 13
14. 14. Step 3: Compute the length of the LCS Procedure 1 LCS-length takes two Sequences X = (x1, x2,…, xm) and Y = (y1, y2,…, yn) as input. Procedure 2 It stores the c[i, j] values in a table c[0..m, 0..n] and it computes the entries in row-major order. Procedure 3 Table b[1..m, 1..n] to construct an optimal solution. b[i, j] points to the table entry corresponding to the optimal solution chosen when computing c[i, j] Procedure 4 Return the b and c tables; c[m, n] contains the length of an LCS X and Y14
15. 15. LCS-Length(X, Y) 1 m = X.length 2 n = Y.length 3 let b[1..m, 1..n] and c[0..m, 0..n] be new tables. 4 for i 1 to m do  5 c[i, 0] = 0 6 for j 1 to n do  7 c[0, j] = 0 8 for i 1 to m do  9 for j 1 to n do  10 if xi ==yj  11 c[i, j] = c[i-1, j-1]+1  12 b[i, j] = “ ”   13 else if c[i-1, j] ≥ c[i, j-1]  14 c[i, j] = c[i-1, j]  15 b[i, j] = “ ”  16 else 17 c[i, j] = c[i, j-1]  18 b[i, j] = “ ” 19 return c and b 15
16. 16. The table produced by LCS-Length on the sequences X = (A, B, C, B, D, A, B) and Y = (B, D, C, A, B, A). 16 The running time of the procedure is O(mn), since each table entry table O(1) time to compute
17. 17. Step 4: Construct an optimal LCS PRINT-LCS(b, X, i, j) PRINT-LCS(b, X, X.length, Y.length) 1 if i == 0 or j == 0 2 return 3 if b[i, j] == “ ” 4 PRINT-LCS(b,X,i-1, j-1) 5 print Xi 6 else if b[i, j] == “ ” 7 PRINT-LCS(b,X,i-1, j) 8 else PRINT-LCS(b,X,i, j-1) This procedure prints BCBA. The procedure takes time O(m+n)
18. 18. Example X = <A, B, C, B, A> Y = <B, D, C, A, B> We will fill in the table in row-major order starting in the upper left corner using the following formulas:
19. 19. Example X = <A, B, C, B, A> Y = <B, D, C, A, B> We will fill in the table in row-major order starting in the upper left corner using the following formulas:
20. 20. Answer Thus the optimal LCS length is c[m,n] = 3. Optimal LCS starting at c[5,5] we get Z = <B, C, B> Alternatively start at c[5,4] we would produce Z = <B, C, A>. *Note that the LCS is not unique but the optimal length of the LCS is. 20
21. 21. Reference Lecture 13: Dynamic Programming - Longest Common Subsequence http://faculty.ycp.edu/ ~dbabcock/cs360/lectures/lecture13.html http://www.csie.ntnu.edu.tw/~u91029/ LongestCommonSubsequence.html Longest common subsequence (Cormen et al., Sec. 15.4) https://www.youtube.com/watch?v=Wv1y45iqsbk https://www.youtube.com/watch?v=wJ-rP9hJXO0