1 / 34

Divide-and-Conquer

Divide-and-Conquer. Divide-and-Conquer. It’s a technique instead of an algorithm Recursive in structure Divide the problem into several smaller sub-problems that are similar to the original but smaller in size

hertel
Download Presentation

Divide-and-Conquer

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. Divide-and-Conquer Z. Guo

  2. Divide-and-Conquer • It’s a technique instead of an algorithm • Recursive in structure • Divide the problem into several smaller sub-problems that are similar to the original but smaller in size • Conquer the sub-problems by solving them recursively. If they are small enough, just solve them in a straightforward manner. • Combine the solutions to create a solution to the original problem Z. Guo

  3. An Example: Merge Sort Sorting Problem:Sort a sequence or n elements into non-decreasing order. • Divide: Divide the n-element sequence to be sorted into two subsequences of n/2 elements each • Conquer: Sort the two subsequences recursively using merge sort. • Combine: Merge the two sorted subsequences to produce the sorted answer. Z. Guo

  4. 18 18 26 26 32 32 6 6 43 43 15 15 9 9 1 1 43 43 15 15 9 9 1 1 18 18 26 26 32 32 6 6 18 18 26 26 32 32 6 6 43 43 15 15 9 9 1 1 18 26 32 6 43 15 9 1 18 26 32 6 43 15 9 1 Merge Sort – Example Original Sequence Sorted Sequence 1 6 9 15 18 26 32 43 6 32 26 26 32 6 18 18 9 15 1 43 1 9 15 43 26 32 15 18 6 43 18 26 6 32 15 43 1 9 1 9 15 26 32 1 18 26 32 6 43 15 9 1 18 15 43 9 6 26 9 1 32 6 18 43 6 43 18 26 32 1 15 9 Z. Guo

  5. Merge-Sort (A, p, r) INPUT: a sequence of n numbers stored in array A OUTPUT: an ordered sequence of n numbers • MergeSort (A, p, r) // sort A[p..r] by divide & conquer • ifp < r • thenq (p+r)/2 • MergeSort (A, p, q) • MergeSort (A, q+1, r) • Merge (A, p, q, r) // merges A[p..q] with A[q+1..r] Initial Call: MergeSort(A, 1, n) Z. Guo

  6. Sentinels, to avoid having to check if either subarray is fully copied at each step. Procedure Merge Input: Array containing sorted subarrays A[p..q] and A[q+1..r]. Output: Merged sorted subarray in A[p..r]. Merge(A, p, q, r) 1 n1 q – p + 1 2 n2 r –q • fori  1 ton1 • doL[i]  A[p + i – 1] • forj  1 ton2 • doR[j]  A[q + j] • L[n1+1]   • R[n2+1]   • i  1 • j  1 • fork p tor • doifL[i]  R[j] • thenA[k]  L[i] • i  i + 1 • elseA[k]  R[j] • j  j + 1 Z. Guo

  7. Merge – Example A … 6 8 26 32 1 9 42 43 … 1 6 8 9 26 32 42 43 k k k k k k k k k L R 6 8 26 32  6 8 26 32 1 9 42 43  1 9 42 43 i i i i i j j j j j Z. Guo

  8. Correctness of Merge Loop Invariant for the for loop At the start of each iteration of the for loop: Subarray A[p..k – 1] contains the k –p smallest elements of L and R in sorted order. L[i] and R[j] are the smallest elements of L and R that have not been copied back into A. Merge(A, p, q, r) 1 n1 q – p + 1 2 n2 r –q • fori  1 ton1 • doL[i]  A[p + i – 1] • forj  1 ton2 • doR[j]  A[q + j] • L[n1+1]   • R[n2+1]   • i  1 • j  1 • fork p tor • doifL[i]  R[j] • thenA[k]  L[i] • i  i + 1 • elseA[k]  R[j] • j  j + 1 • Initialization: • Before the first iteration: • A[p..k – 1] is empty. • i = j = 1. • L[1] and R[1] are the smallest • elements of L and R not copied to A. Z. Guo

  9. Correctness of Merge • Maintenance: • Case 1:L[i]  R[j] • By LI, A contains p – k smallest elements of L and R in sorted order. • By LI, L[i] and R[j] are the smallest elements of L and R not yet copied into A. • Line 13 results in A containing p – k + 1 smallest elements (again in sorted order). • Incrementing i and k reestablishes the LI for the next iteration. • Similarly for L[i] > R[j]. Merge(A, p, q, r) 1 n1 q – p + 1 2 n2 r –q • fori  1 ton1 • doL[i]  A[p + i – 1] • forj  1 ton2 • doR[j]  A[q + j] • L[n1+1]   • R[n2+1]   • i  1 • j  1 • fork p tor • doifL[i]  R[j] • thenA[k]  L[i] • i  i + 1 • elseA[k]  R[j] • j  j + 1 • Termination: • On termination, k = r + 1. • By LI, A contains r – p + 1 smallest • elements of L and R in sorted order. • L and R together contain r – p + 3 elements. • All but the two sentinels have been copied • back into A. Z. Guo

  10. Analysis of Merge Sort • Running time T(n) of Merge Sort: • Divide: computing the middle takes (1) • Conquer: solving 2 subproblems takes 2T(n/2) • Combine: merging n elements takes (n) • Total: T(n) = (1)if n = 1 T(n) = 2T(n/2) + (n)if n > 1  T(n) = (n lg n) (CLRS, Chapter 4) • MergeSort (A, p, r) // sort A[p..r] by divide & conquer • ifp < r • thenq (p+r)/2 • MergeSort (A, p, q) • MergeSort (A, q+1, r) • Merge (A, p, q, r) // merges A[p..q] with A[q+1..r] Z. Guo

  11. Recurrence Relations • Recurrences (Chapter 4) • Substitution Method • Iteration Method • Master Method • Arising from Divide and Conquer (e.g. MERGE-SORT) T(n) =  (1) if n  c T(n) = a T(n/b) + D(n) + C(n) otherwise Z. Guo

  12. Substitution Method • Guessing the form of the solutions, then using mathematical induction to find the constants and show the solution works. • It works well when it is easy to guess. But, there is no general way to guess the correct solution. Z. Guo

  13. An Example • Solve: T(n) = 3T(n/3) + n T(n)   = =  c n lg n GUESS Z. Guo

  14. An Example • Solve: T(n) = 3T(n/3) + n T(n)  3c n/3 lg n/3 + n  c n lg (n/3) + n = c n lg n - c n lg3 + n = c n lg n - n (c lg 3 - 1)  c n lg n * The last step istrue for c 1 / lg3. Z. Guo

  15. Making a Good Guess • Guessing a similar solution to the one that you have seen before • T(n) = 3T(n/3 + 5) + n similar to T(n) = 3T(n/3) + n when n is large, the difference between n/3 and (n/3 + 5) is insignificant • Another way is to prove loose upper and lower bounds on recurrence and then reduce the range of uncertainty. • Start with T(n) = (n) & T(n) = O(n2) T(n) =  (n log n) Z. Guo

  16. Changing Variables • Use algebraic manipulation to turn an unknown recurrence similar to what you have seen before. • Consider T(n) = 2T(n1/2) + lg n Z. Guo

  17. Changing Variables • Use algebraic manipulation to turn an unknown recurrence similar to what you have seen before. • Consider T(n) = 2T(n1/2) + lg n • Rename m = lg n and we have T(2m) = 2T(2m/2) + m • Set S(m) = T(2m) and we have S(m) = 2S(m/2) + m  S(m) = O(m lg m) • Changing back from S(m) to T(n), we have T(n) = T(2m) = S(m) = O(m lg m) = O(lg n lg lg n) Z. Guo

  18. Avoiding Pitfalls • Be careful not to misuse asymptotic notation. For example: • We can falsely prove T(n) = O(n) by guessing T(n)  c n for T(n) = 2T(n/2) + n T(n)  2c n/2 + n  c n + n = O(n) Wrong! • The err is that we haven’t proved T(n)  c n Z. Guo

  19. Exercises • Solution of T(n) = T(n/2) + 1 isO(lg n) • Solution of T(n) = 2T(n/2+ 17) + n isO(n lg n) • Solve T(n) = 2T(n1/2) + 1 by making a change of variables. Don’t worry whether values are integral. Z. Guo

  20. Recursion-tree Method • Making a good guess is sometimes difficult ... • Use recursion trees to devise good guesses. • Recursion Trees • Show successive expansions of recurrences using trees. • Keep track of the time spent on the subproblems of a divide and conquer algorithm. • Help organize the algebraic bookkeeping necessary to solve a recurrence. Comp 550

  21. Recursion Tree – Example • Running time of Merge Sort: T(n) = (1)if n = 1 T(n) = 2T(n/2) + (n)if n > 1 • Rewrite the recurrence as T(n) = cif n = 1 T(n) = 2T(n/2) + cnif n > 1 c > 0:Running time for the base case and time per array element for the divide and combine steps. Comp 550

  22. cn Cost of divide and merge. cn cn/2 cn/2 T(n/2) T(n/2) T(n/4) T(n/4) T(n/4) T(n/4) Cost of sorting subproblems. Recursion Tree for Merge Sort For the original problem, we have a cost of cn, plus two subproblems each of size (n/2) and running time T(n/2). Each of the size n/2 problems has a cost of cn/2 plus two subproblems, each costing T(n/4). T(n) = 2T(n/2) + cn Comp 550

  23. cn cn/2 cn/2 cn/4 cn/4 cn/4 cn/4 c c c c c c Recursion Tree for Merge Sort Continue expanding until the problem size reduces to 1. cn cn lg n cn cn Total : cnlgn+cn Comp 550

  24. cn cn/2 cn/2 cn/4 cn/4 cn/4 cn/4 c c c c c c Recursion Tree for Merge Sort Continue expanding until the problem size reduces to 1. • Each level has total cost cn. • Each time we go down one level, the number of subproblems doubles, but the cost per subproblem halves  cost per level remains the same. • There are lg n + 1 levels, height is lg n. (Assuming n is a power of 2.) • Can be proved by induction. • Total cost = sum of costs at each level = (lg n + 1)cn = cnlgn + cn = (n lgn). Comp 550

  25. Recursion Trees and Recurrences • Useful even when a specific algorithm is not specified • For T(n) = 2T(n/2) + n2, we have Z. Guo

  26. Recursion Trees T(n) = (n2) Z. Guo

  27. Recursion Trees – Caution Note • Recursion trees only generate guesses. • Verify guesses using substitution method. • If careful when drawing out a recursion tree and summing the costs, can be used as direct proof. Comp 550

  28. Recursion Trees • Exercise T(n) = T(n/2) + T(n/4) + n2 Z. Guo

  29. The Master Method • Based on the Master theorem. • “Cookbook” approach for solving recurrences of the form T(n) = aT(n/b) + f(n) • a  1, b > 1 are constants. • f(n) is asymptotically positive. • T(n) is defined for nonnegative integers • n/b may not be an integer, but we ignore floors and ceilings. Why? Comp 550

  30. The Master Theorem • Theorem 4.1 • Let a  1 and b > 1be constants, let f(n) be a function, and Let T(n) be defined on nonnegative integers by the recurrence T(n) = aT(n/b) + f(n), where we can replace n/b by n/b or n/b. T(n) can be bounded asymptotically in three cases: • If f(n) = O(nlogba–) for some constant  > 0, then T(n) = (nlogba). • If f(n) = (nlogba), then T(n) = (nlogbalg n). • If f(n) = (nlogba+) for some constant  > 0, and if, for some constant c < 1 and all sufficiently large n, we have a·f(n/b)  c f(n), then T(n) = (f(n)). We’ll return to recurrences as we need them… If none of these three cases apply, you’re on your own. Comp 550

  31. Master Theorem Let a  1 and b >1 be constants andlet T(n) be the recurrence T(n) = a T(n/b) + c nk defined for n 0. 1. If a>bk, then T(n) = ( nlogba ). 2. Ifa=bk, then T(n) = ( nk lg n ). 3. If a< bk, then T(n) = ( nk ). Z. Guo

  32. Examples • T(n) = 16T(n/4) + n • a = 16, b = 4, thus nlogba = nlog416= (n2) • f(n) = n = O(nlog416-)where  = 1 case 1. • Therefore, T(n) = (nlogba)=(n2) • T(n) = T(3n/7) + 1 • a = 1, b=7/3, and nlogba = nlog7/31 = n0 = 1 • f(n) = 1 = (nlogba)case 2. • Therefore, T(n) = (nlogba lg n) = (lg n) Z. Guo

  33. Examples (Cont.) • T(n) = 3T(n/4) + n lg n • a = 3, b=4, thus nlogba = nlog43 = O(n0.793) • f(n) = n lg n = (nlog43 + )where 0.2case 3. • Therefore, T(n) = (f(n)) = (n lg n) • T(n) = 2T(n/2) + n lg n • a = 2, b=2, f(n) = n lg n, and nlogba = nlog22 = n • f(n)is asymptotically larger thannlogba, but not polynomially larger. The ratio lg n is asymptotically less than n for any positive . Thus, the Master Theorem doesn’tapply here. Z. Guo

  34. Exercises • Use the Master Method to solve the following: • T(n) = 4T(n/2) + n • T(n) = 4T(n/2) + n2 • T(n) = 4T(n/2) + n3 • Try this if you finished the ones above:T(n) = 4T(n/2) + n2/lgn Z. Guo

More Related