1 / 26

Dynamic Programming

Dynamic Programming. Longest Common Subsequence. Dynamic Programming(DP). A powerful design technique for optimization problems Related to divide and conquer However, due to the nature of DP problems, standard divide-and-conquer solution are not efficient. Dynamic Programming(DP).

mccormickt
Download Presentation

Dynamic Programming

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Dynamic Programming Longest Common Subsequence

  2. Dynamic Programming(DP) • A powerful design technique for optimization problems • Related to divide and conquer • However, due to the nature of DP problems, standard divide-and-conquer solution are not efficient

  3. Dynamic Programming(DP) • Main question: How to set up the subproblem structure? • For DP to be applicable to an optimization problem • Optimal substructure: for the global problem to be solved optimally, each subproblem should be solved optimally • Polynomially many subproblems • Overlapping subproblems

  4. Longest Common Subsequence (LCS) • Application: searching for a substring or pattern in a large piece of text • Not necessarily exact text but something similar • Method for measuring degree of similarity: LCS

  5. Longest Common Subsequence (LCS) • Given two sequences x[1 . . m] and y[1 . . n], find a longest subsequence common to them both. x: A B C B D A B y: B D C A B A

  6. Longest Common Subsequence (LCS) • Given two sequences x[1 . . m] and y[1 . . n], find a longest subsequence common to them both. x: A B C B D A B y: B D C A B A LCS(x,y) = BCBA

  7. LCS • Not always unique X : A B C Y: B A C

  8. LCS • Not always unique X : A B C Y: B A C

  9. Brute Force Solution? • Check every subsequence of x[1 . . m] to see if it is also a subsequence of y[1 . .n]

  10. Brute Force Solution? • Check every subsequence of x[1 . . m] to see if it is also a subsequence of y[1 . .n] Analysis • Checking = O(n) time per subsequence. • subsequences of x(each bit-vector of length m determines a distinct subsequence of x). Thus, Exponential time.

  11. DP Formulation for LCS • Subproblems? • Consider all pairs of prefixes • A prefix of a sequence is just an initial string of characters • Let denote the prefix of X with i characters • Let denote the empty sequence x: A B C B D A B

  12. DP Formulation • Compute the LCS for every possible pair of prefixes • C[i,j]: length of the LCS of and • Then, C[m,n]: length of LCS of X and Y x: A B C B D A B y: B D C A B A

  13. Recursive formulation • C[i,0] = C[0,j] = 0 // base case • How can I compute C[i,j] using solutions to subproblems?

  14. Recursive formulation Two cases • Last characters match: if X[i] = Y[j] LCS must end with this same character x: A B C B D A B y: B D C A B A i X A j Y A

  15. Recursive formulation Two cases • Last characters match: if X[i] = Y[j] LCS must end with this same character C[i,j] = C[i-1, j-1] + 1 LCS ( ) = LCS( ) + X[i] i X A j Y A

  16. i i i X X X B B B j j j Y Y Y A A A Recursive formulation (2) Last characters DO NOT match: LCS( ) //skip x[i] LCS( ) //skip Y[j] C[i,j] = max (C[i, j-1], C[i-1,j])

  17. A recursive algorithm LCS(x, y, i, j) { if x[i] = y[ j ] then c[i, j ] ←LCS(x, y, i–1, j–1) + 1 else c[i, j ] ←max{LCS(x, y, i–1, j), LCS(x, y, i, j–1) } • Worst-case:x[i] ≠y[ j ], in which case the algorithm evaluates two subproblems, each with only one parameter decremented.

  18. 3,4 2,4 3,3 1,4 2,3 2,3 3,2 Recursion tree • Height = O(m+n) • Running time = potentially exponential!

  19. BUT, we keep recomputing the same subproblems 3,4 2,4 3,3 1,4 2,3 2,3 3,2

  20. Overlapping subproblems • A recursive solution contains a “small” number of distinct subproblems repeated many times • The number of distinct LCS subproblems for two strings of lengths m andnis onlymn

  21. Store the solutions of subproblems • After computing a solution to a subproblem, store it in a table. • C[0..m,0..n] that stores lengths of LCS • Keep also a helper array B[0..m,0..n] to store some pointers to extract the LCS later

  22. LCS(x[1..m],y[1..n]) { int C[0..m,0..n]; int B[0..m,0..n] for i=0 to m C[i,0] = 0; B[i,0] = UP for j=0 to n C[0,j] = 0; B[0,j] = LEFT for i=1 to m for j=1 to n if x[i] == y[j] C[i,j]=c[i-1, j-1] +1; B[i,j] = DIAG; else if (C[i-1,j] >= C[i, j-1]) C[i,j]=C[i-1, j]; B[i,j] = UP; else C[i,j]=C[i, j-1]; B[i,j] = LEFT; return C[m,n]; } O(mn)

  23. B D C B 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 1 1 2 2 0 1 2 2 2 0 1 2 2 3 B A C D B Compute tables bottom-up Y Y B D C B 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 1 1 2 2 0 1 2 2 2 0 1 2 2 3 B A X X C D B Length Table: C Tables C and B

  24. B D C B 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 1 1 2 2 0 1 2 2 2 0 1 2 2 3 B A C D B Compute tables bottom-up Y Y B D C B 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 1 1 2 2 0 1 2 2 2 0 1 2 2 3 B A X X C D B Length Table: C Tables C and B Start from B[m,n], follow pointers Extract entries with DIAG ( ) LCS for above example: BCB

  25. ExtractLCS(B, X, i, j) { //initially called with (B, X, m, n) if i==0 OR j== 0 return; if B[i,j] == DIAG ExtractLCS(B, X, i-1, j-1); print X[i] else if B[i,j] == UP extractLCS(B, X, i-1, j); else // LEFT extractLCS(B, X, i, j-1) }

  26. Example of LCF • https://www.youtube.com/watch?v=NnD96abizww

More Related