1 / 22

Chapter 6 Dynamic Programming

Chapter 6 Dynamic Programming. Algorithmic Paradigms. Greedy. Build up a solution incrementally, optimizing some local criterion.

scot
Download Presentation

Chapter 6 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. Chapter 6Dynamic Programming

  2. Algorithmic Paradigms • Greedy. Build up a solution incrementally, optimizing some local criterion. • Divide-and-conquer. Break up a problem into sub-problems, solve each sub-problem independently, and combine solution to sub-problems to form solution to original problem. • Dynamic programming.Break up a problem into a series of overlapping sub-problems, and build up solutions to larger and larger sub-problems.

  3. Dynamic Programming Applications • Areas. • Bioinformatics. • Control theory. • Information theory. • Operations research. • Computer science: theory, graphics, AI, compilers, systems, …. • Some famous dynamic programming algorithms. • Linux diff for comparing two files. • Smith-Waterman for genetic sequence alignment. • Bellman-Ford for shortest path routing in networks. • Cocke-Kasami-Younger for parsing context free grammars.

  4. Knapsack Problem • Knapsack problem. • Given n objects and a "knapsack." • Item i weighs wi > 0 kilograms and has value vi > 0. • Knapsack has capacity of W kilograms. • Goal: fill knapsack so as to maximize total value. • Ex: { 3, 4 } has value 40. • Greedy: repeatedly add item with maximum ratio vi / wi. • Ex: { 5, 2, 1 } achieves only value = 35  greedy not optimal. # value weight 1 1 1 2 6 2 W = 11 3 18 5 4 22 6 5 28 7

  5. Dynamic Programming: False Start • Def. OPT(i) = max profit subset of items 1, …, i. • Case 1: OPT does not select item i. • OPT selects best of { 1, 2, …, i-1 } • Case 2: OPT selects item i. • accepting item i does not immediately imply that we will have to reject other items • without knowing what other items were selected before i,we don't even know if we have enough room for i • Conclusion. Need more sub-problems!

  6. Dynamic Programming: Adding a New Variable • Def. OPT(i, w) = max profit subset of items 1, …, i with weight limit w. • Case 1: OPT does not select item i. • OPT selects best of { 1, 2, …, i-1 } using weight limit w • Case 2: OPT selects item i. • new weight limit = w – wi • OPT selects best of { 1, 2, …, i–1 } using this new weight limit

  7. Knapsack Problem: Bottom-Up • Knapsack. Fill up an n-by-W array. Input: n, W, w1,…,wN, v1,…,vN for w = 0 to W M[0, w] = 0 for i = 1 to n for w = 1 to W if (wi > w) M[i, w] = M[i-1, w] else M[i, w] = max {M[i-1, w], vi + M[i-1, w-wi ]} return M[n, W]

  8. 0 1 2 3 4 5 6 7 8 9 10 11  0 0 0 0 0 0 0 0 0 0 0 0 { 1 } 0 1 1 1 1 1 1 1 1 1 1 1 { 1, 2 } 0 1 6 7 7 7 7 7 7 7 7 7 { 1, 2, 3 } 0 1 6 7 7 18 19 24 25 25 25 25 { 1, 2, 3, 4 } 0 1 6 7 7 18 22 24 28 29 29 40 { 1, 2, 3, 4, 5 } 0 1 6 7 7 18 22 28 29 34 34 40 Item Value Weight 1 1 1 2 6 2 3 18 5 4 22 6 5 28 7 Knapsack Algorithm W + 1 n + 1 OPT: { 4, 3 } value = 22 + 18 = 40 W = 11

  9. Knapsack Problem: Running Time • Running time. (n W). • Not polynomial in input size! • "Pseudo-polynomial." • Decision version of Knapsack is NP-complete. [Chapter 8] • Knapsack approximation algorithm. There exists a poly-time algorithm that produces a feasible solution that has value within 0.01% of optimum. [Section 11.8]

  10. String Similarity • How similar are two strings? • ocurrance • occurrence o c u r r a n c e - o c c u r r e n c e 6 mismatches, 1 gap o c - u r r a n c e o c c u r r e n c e 1 mismatch, 1 gap o c - u r r - a n c e o c c u r r e - n c e 0 mismatches, 3 gaps

  11. Edit Distance • Applications. • Basis for Linux diff. • Speech recognition. • Computational biology. • Edit distance. [Levenshtein 1966, Needleman-Wunsch 1970] • Gap penalty ; mismatch penalty pq. • In general, 2 >= pq. • Cost = sum of gap and mismatch penalties. C T G A C C T A C C T - C T G A C C T A C C T C C T G A C T A C A T C C T G A C - T A C A T TC + GT + AG+ 2CA 2+ CA

  12. Sequence Alignment • Goal: Given two strings X = x1 x2 . . . xm and Y = y1 y2 . . . yn of symbols, find alignment of minimum cost. • Def. An alignment M is a set of ordered pairs xi-yj such that each symbol occurs in at most one pair and no crossings. The number of xi and yj that don’t appear in M is the number of gaps. • Def. The pair xi-yj and xi'-yj'cross if i < i', but j > j'. • Ex:CTACCG vs. TACATG.Sol: M = x2-y1, x3-y2, x4-y3, x5-y4, x6-y6. x1 x2 x3 x4 x5 x6 C T A C C - G - T A C A T G y1 y2 y3 y4 y5 y6

  13. Sequence Alignment: Problem Structure • Def. OPT(i, j) = min cost of aligning strings x1 x2 . . . xi and y1 y2 . . . yj. • Case 1: OPT matches xi-yj. • pay mismatch for xi-yj + min cost of aligning two stringsx1 x2 . . . xi-1 and y1 y2 . . . yj-1 • Case 2a: OPT leaves xi unmatched. • pay gap for xi and min cost of aligning x1 x2 . . . xi-1 and y1 y2 . . . yj • Case 2b: OPT leaves yj unmatched. • pay gap for yj and min cost of aligning x1 x2 . . . xi and y1 y2 . . . yj-1

  14. Sequence Alignment: Algorithm • Analysis. (mn) time and space. • English words or sentences: m, n  10. • Computational biology: m = n = 100,000. • 10 billions ops OK, but 10GB array? Alignment(m, n, x1x2...xm, y1y2...yn, , ) { // A[0..m,0..n]: int array for i = 0 to m A[i, 0] = i for j = 1 to n A[0, j] = j for i = 1 to m A[i, j] = min([xi, yj] + A[i-1, j-1],  + A[i-1, j],  + A[i, j-1]) return A[m, n] }

  15. Sequence Alignment: Algorithm Alignment(m, n, x1x2...xm, y1y2...yn, , ) { // A[0..m,0..n]: int array for i = 0 to m A[i, 0] = i for j = 1 to n A[0, j] = j for i = 1 to m A[i, j] = min([xi, yj] + A[i-1, j-1],  + A[i-1, j],  + A[i, j-1]) return A[m, n] } Assuming  = 1 [xi, yj] = 0 if xi=yj [xi, yj] = 1 otherwise

  16. Subequence Alignment • Goal: Given two strings X = x1 x2 . . . xm and Y = y1 y2 . . . yn of symbols, find alignment of X and a substring of Y with minimum cost. • Ex:CTACCG vs. TXYTACATGAH.Sol: Substring is TACATG and M = x2-y4, x3-y5, x4-y6, x5-y7, x6-y9.

  17. Sequence Alignment: Problem Structure • Def. OPT(i, j) = min cost of aligning strings x1 x2 . . . xi and y1 y2 . . . yj. • Case 1: OPT matches xi-yj. • pay mismatch for xi-yj + min cost of aligning two stringsx1 x2 . . . xi-1 and y1 y2 . . . yj-1 • Case 2a: OPT leaves xi unmatched. • pay gap for xi and min cost of aligning x1 x2 . . . xi-1 and y1 y2 . . . yj • Case 2b: i < m and OPT leaves yj unmatched. • pay gap for yj and min cost of aligning x1 x2 . . . xi and y1 y2 . . . yj-1 • Case 2c: i == m and OPT leaves yj unmatched. • pay 0 for yj and min cost of aligning x1 x2 . . . xi and y1 y2 . . . yj-1

  18. Subequence Alignment: Algorithm • Analysis. (mn) time and space. Alignment(m, n, x1x2...xm, y1y2...yn, , ) { // A[0..m,0..n]: int array for i = 0 to m A[i, 0] = i for j = 1 to n A[0, j] = 0 for i = 1 to m - 1 A[i, j] = min([xi, yj] + A[i-1, j-1],  + A[i-1, j],  + A[i, j-1]) A[m, j] = min([xm, yj] + A[m-1, j-1],  + A[m-1, j], A[m, j-1]) return A[m, n] }

  19. Longest common subsequence • The longest common subsequence (not substring) between “democrat” and “republican” is eca. • A common subsequence is defined by all the identical character • matches in an alignment of two strings. • To maximize the number of such matches, we must prevent substitution of non-identical characters, that is, 2 <= pq for p != q. • A[i, j] = min([xi, yj] + A[i-1, j-1], •  + A[i-1, j], •  + A[i, j-1])

  20. Maximum Monotone Subsequence • A numerical sequence is monotonically increasing if the ith element is at least as big as the (i - 1)st element. • The maximum monotone subsequence problem seeks to delete the fewest number of elements from an input string S to leave a monotonically increasing subsequence. • Ex: A longest increasing subsequence of “243519698” is “24569.” • Let X be the input sequence and Y be the sorted input sequence. Then a longest increasing subsequence of X is also a longest common subsequence of X and Y, and vice versa. • Using the previous idea, we can solve this problem in O(n2) space and time. Can we do better?

  21. Maximum Monotone Subsequence • A numerical sequence is monotonically increasing if the ith element is at least as big as the (i - 1)st element. Given X = x1 x2 . . . xn find the longest monotonically increasing subsequence of X. • Let OPT(i) be the longest monotonically increasing subsequence ending with xi. Then • OPT(1) = 1 and • OPT(i) = max(OPT(j)+1 : j < i and xj < xi ) MonotoneSubsequence(x1x2...xn) { // A[1..n]: int array for i = 1 to n { A[i] = 1 for j = 1 to i - 1 if (xi >= xj) A[i] = max(A[i], A[j]+1) } return max(A[1..n]) } // O(n) space, O(n2) time

  22. Maximum Monotone Subsequence • MonotoneSubsequence returns the length of maximum monotone subsequence. How to return the maximum monotone subsequence? MonotoneSubsequence2(x1x2...xn) { y = MonotoneSubsequence(x1x2...xn) for k = 1 to n if (A[k] == y) i = k; S = []; while (i > 0) { S = xi + S for j = i – 1 to 1 if (xi >= xj && A[i] == A[j]+1) { i = j; break; } if (j < 1) break; } return S } // O(n) time

More Related