# Design and Analysis of Algorithms

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1 Design and Analysis of Algorithms CSE 5311 Lecture 3 Divide-and-Conquer Junzhou Huang, Ph.D. Department of Computer Science and Engineering CSE5311 Design and Analysis of Algorithms 1

2 Reviewing: Θ-notation Definition: Θ(g(n)) = { f (n) : there exist positive constants c 1, c 2, and n 0 such that 0 c 1 g(n) f (n) c 2 g(n) for all n n 0 } Basic Manipulations: Drop low-order terms; ignore leading constants. Example: 3n n 2 5n = Q(n 3 ) CSE5311 Design and Analysis of Algorithms 2

3 Reviewing: Insertion Sort Analysis Worst case: Input reverse sorted. T( n) = n j= 2 Θ( j) =Θ ( n2 ) Average case: All permutations equally likely. [arithmetic series] T( n) = n j= 2 Θ( j/2) =Θ ( n2 ) Is insertion sort a fast sorting algorithm? Moderately so, for small n. Not at all, for large n. CSE5311 Design and Analysis of Algorithms 3

4 Reviewing: Recurrence for Merge Sort T(n) = Θ(1) if n = 1; 2T(n/2) + Θ(n) if n > 1. We shall usually omit stating the base case when T(n) = Θ(1) for sufficiently small n, but only when it has no effect on the asymptotic solution to the recurrence. Next Lecture will provide several ways to find a good upper bound on T(n). CSE5311 Design and Analysis of Algorithms 4

5 Reviewing: Recursion Tree Solve T(n) = 2T(n/2) + cn, where c > 0 is constant. cn cn cn/2 cn/2 cn h = lg n cn/4 cn/4 cn/4 cn/4 cn Θ(1) #leaves = n Θ(n) Total = Θ(n lg n) CSE5311 Design and Analysis of Algorithms 5

6 Solving Recurrences Recurrence The analysis of integer multiplication from last lecture required us to solve a recurrence Recurrences are a major tool for analysis of algorithms Divide and Conquer algorithms which are analyzable by recurrences. Three steps at each level of the recursion: Divide the problem into a number of subproblems that are smaller instances of the same problem. Conquer the subproblems by solving them recursively. If the subproblem sizes are small enough, however, just solve the subproblems in a straightforward manner. Combine the solutions to the subproblems into the solution for the original problem. CSE5311 Design and Analysis of Algorithms 6

7 Recall: Integer Multiplication Let X = A B and Y = C D where A,B,C and D are n/2 bit integers Simple Method: XY = (2 n/2 A+B)(2 n/2 C+D) Running Time Recurrence T(n) < 4T(n/2) + Θ(n) How do we solve it? CSE5311 Design and Analysis of Algorithms 7

8 Substitution Method The most general method: 1. Guess the form of the solution. 2. Verify by induction. 3. Solve for constants. Example: T(n) = 4T(n/2) + Θ(n) [Assume that T(1) = Θ(1).] Guess O(n 3 ). (Prove O and Ω separately.) Assume that T(k) ck 3 for k < n. Prove T(n) cn 3 by induction. CSE5311 Design and Analysis of Algorithms 8

9 Example of substitution T ( n ) = = = 4T 4c( n ( c cn cn / ( n 3 3 / / 2) n 2) (( 2) 3 3 c + + / + Θ(n) Θ(n) Θ(n) 2) n desired 3 Θ(n) desired residual We can imagine Θ(n)=100n. Then, whenever (c/2)n 3 100n 0, for example, if c 200 and n 1. ) residual CSE5311 Design and Analysis of Algorithms 9

10 Example We must also handle the initial conditions, that is, ground the induction with base cases. Base: T(n) = Θ(1) for all n < n 0, where n 0 is a suitable constant. For 1 n < n 0, we have Θ(1) cn 3, if we pick c big enough. This bound is not tight! CSE5311 Design and Analysis of Algorithms 10

11 A Tighter Upper Bound? We shall prove that T(n) = O(n 2 ). Assume that T(k) ck 2 for k < n: T ( n ) = 4T ( n / 2) + 100n cn cn n 2 for no choice of c > 0. Lose! CSE5311 Design and Analysis of Algorithms 11

12 A Tighter Upper Bound! IDEA: Strengthen the inductive hypothesis. Subtract a low-order term. Inductive hypothesis: T(k) c 1 k 2 c 2 k for k < n. T ( n ) = = = 4T 4( c c c c n n n ( n / ( n 2) 2) 2c ( c ( n 2)) + if c 2 > 100. Pick c 1 big enough to handle the initial conditions. / c c n n 2 n 100n + c 2 100n 2 n / 100n ) 100n CSE5311 Design and Analysis of Algorithms 12

13 Recursion-tree Method A recursion tree models the costs (time) of a recursive execution of an algorithm. The recursion tree method is good for generating guesses for the substitution method. The recursion-tree method can be unreliable, just like any method that uses ellipses ( ). However, the recursion-tree method promotes intuition CSE5311 Design and Analysis of Algorithms 13

14 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : CSE5311 Design and Analysis of Algorithms 14

15 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : T(n) CSE5311 Design and Analysis of Algorithms 15

16 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : n 2 T(n/4) T(n/2) CSE5311 Design and Analysis of Algorithms 16

17 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : n 2 (n/4) 2 (n/2) 2 T(n/16) T(n/8) T(n/8) T(n/4) CSE5311 Design and Analysis of Algorithms 17

18 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : n 2 (n/4) 2 (n/2) 2 (n/16) 2 (n/8) 2 (n/8) 2 (n/4) 2 Θ(1) CSE5311 Design and Analysis of Algorithms 18

19 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : n 2 n 2 (n/4) 2 (n/2) 2 (n/16) 2 (n/8) 2 (n/8) 2 (n/4) 2 Θ(1) CSE5311 Design and Analysis of Algorithms 19

20 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : n 2 (n/4) 2 (n/2) 2 (n/16) 2 (n/8) 2 (n/8) 2 (n/4) 2 n2 5 n 2 16 Θ(1) CSE5311 Design and Analysis of Algorithms 20

21 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : n 2 (n/4) 2 (n/2) 2 (n/16) 2 (n/8) 2 (n/8) 2 (n/4) 2 n2 5 n n Θ(1) CSE5311 Design and Analysis of Algorithms 21

22 Example of Recursion Tree Solve T(n) = T(n/4) + T(n/2) + n 2 : n 2 (n/4) 2 (n/2) 2 (n/16) 2 (n/8) 2 (n/8) 2 (n/4) 2 n2 5 n n Θ(1) Total = n 2 = Θ(n 2 ) ( ( ) ( ) ) L 16 5 CSE5311 Design and Analysis of Algorithms geometric series

23 CSE5311 Design and Analysis of Algorithms 23 Appendix: Geometric Series x x x = L for x < x x x x x n n = L for x 1

24 The Master Method The master method applies to recurrences of the form T(n) = a T(n/b) + f (n), where a 1, b > 1, and f is asymptotically positive. CSE5311 Design and Analysis of Algorithms 24

25 Idea of Master Theorem Recursion tree: f (n) a f (n/b) f (n/b) a h = log b n f (n/b 2 ) f (n/b 2 ) f (n/b 2 ) f (n/b) f (n) a f (n/b) a 2 f (n/b 2 ) Τ (1) #leaves = a h = a log bn = n log ba n log ba Τ (1) CSE5311 Design and Analysis of Algorithms 25

26 Case (I) Compare f (n) with n log ba : 1. f (n) = O(n log ba ε ) for some constant ε > 0. f (n) grows polynomially slower than n log ba (by an n ε factor). Solution: T(n) = Θ(n log ba ). CSE5311 Design and Analysis of Algorithms 26

27 Idea of Master Theorem Recursion tree: f (n) a f (n/b) f (n/b) a h = log b n f (n/b 2 ) f (n/b 2 ) f (n/b 2 ) f (n/b) f (n) a f (n/b) a 2 f (n/b 2 ) Τ (1) f(n)= n log ba ε CASE 1: The weight increases geometrically from the root to the leaves. The leaves hold a constant fraction of the total weight. and a f(n/b)=a (n/b) log ba ε = b ε n log ba ε n log ba Τ (1) Θ(n log ba ) CSE5311 Design and Analysis of Algorithms 27

28 Case (II) Compare f (n) with n log ba : 2. f (n) = Θ(n log ba ) for some constant k 0. f (n) and n log ba grow at similar rates. Solution: T(n) = Θ(n log ba lgn). CSE5311 Design and Analysis of Algorithms 28

29 Idea of Master Theorem Recursion tree: f (n) a f (n/b) f (n/b) a h = log b n f (n/b 2 ) f (n/b 2 ) f (n/b 2 ) f (n/b) f (n) a f (n/b) a 2 f (n/b 2 ) Τ (1) f(n)=n log ba CASE 2: (k = 0) The weight is approximately the same on each of the log b n levels. and af(n/b)=a (n/b) log ba = n log ba n log ba Τ (1) Θ(n log ba lg n) CSE5311 Design and Analysis of Algorithms 29

30 Case (III) Compare f (n) with n log ba : 3. f (n) = Ω(n log ba + ε ) for some constant ε > 0. f (n) grows polynomially faster than n log ba (by an n ε factor), and f (n) satisfies the regularity condition that a f (n/b) c f (n) for some constant c < 1. Solution: T(n) = Θ( f (n) ). CSE5311 Design and Analysis of Algorithms 30

31 Idea of master theorem Recursion tree: Τ (1) f (n) f (n/b) f (n/b) a h = log b n f (n/b 2 ) f (n/b 2 ) f (n/b 2 ) f(n)= n log ba + ε a f (n/b) CASE 3: The weight decreases geometrically from the root to the leaves. The root holds a constant fraction of the total weight. and a f(n/b)=a (n/b) log ba + ε = b -ε n log ba + ε f (n) a f (n/b) a 2 f (n/b 2 ) n log ba Τ (1) Θ( f (n)) CSE5311 Design and Analysis of Algorithms 31

32 Examples Ex. T(n) = 4T(n/2) + n a = 4, b = 2 n log ba = n 2 ; f (n) = n. CASE 1: f(n) = O(n 2 ε ) for ε = 1. T(n) = Θ(n 2 ). Ex. T(n) = 4T(n/2) + n 2 a = 4, b = 2 n log ba = n 2 ; f (n) = n 2. CASE 2: f(n) = Θ(n 2 lg 0 n), that is, k = 0. T(n) = Θ(n 2 lg n). CSE5311 Design and Analysis of Algorithms 32

33 Examples Ex. T(n) = 4T(n/2) + n 3 a = 4, b = 2 n log ba = n 2 ; f (n) = n 3. CASE 3: f(n) = Ω(n 2 + ε ) for ε = 1 and 4(n/2) 3 cn 3 (reg. cond.) for c = ½<1 T(n) = Θ(n 3 ). Ex. T(n) = 4T(n/2) + n 2 /lgn a = 4, b = 2 n log ba = n 2 ; f (n) = n 2 /lgn. Master method does not apply. In particular, for every constant ε > 0, we have n ε = ω(lgn). CSE5311 Design and Analysis of Algorithms 33

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