Big-O notation.
Let
T(n) be given using the recursive formula.
T(n) = T(n-1) + n,
T(1) = 1.
Prove that T(n) = O(n2).
T(n) = T(n-1) + n, T(1) = 1. using substitution method: --------------------------- T(n) = T(n-1) + n = T(n-2) + n-1 + n = T(n-3) + n-2 + n-1 + n = T(1) + 2 + ... + n-2 + n-1 + n = 1 + 2 + ... + n-2 + n-1 + n This is sum of first n natural numbers. It's formula is n(n+1)/2. = n(n+1)/2 = (n2+n)/2 we can ignore lower order terms(n) and constant factors(/2). so, time complexity is O(n2) T(n) = O(n2)
Big-O notation. Let T(n) be given using the recursive formula. T(n) = T(n-1) + n, T(1)...
Analyze and prove the running time of the recursive formula T(n) = n2 + 2T(n/2). (hint- first prove that it is O(n2 logn). Next see if you could improve your answer. We used the recursion tree method to analyze the running time of MergeSort. Use this method to analyze each one of the following recursive formulas, and obtain its solution. Assume T(n) = 1 when n ≤ 1. Analyze and prove the running time of the recursive formula T(n) =...
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Problem 1. Solve the recursive equations with big-O notation. a) T(n)=167(n/2) + n° with T(1)=1. b) T(n) = T(vn+1 with T(1)=T(2)=T(3)=1, where (a) is the largest integer m less than or equal to a. For example [3.1]=3.
Given n distinct items: Assuming n is a power of 2, write down a recursive divide-and-conquer algorithm for solving simultaneous minimum and maximum, using n = 2 as the bottom of the recursion. If we let T(n) denote the number of comparisons done by your algorithm, write down the recurrence relation satisfied by T(n). Solve exactly (without using the “big O” notation) the recurrence relation for T(n), showing all the details of your work.
7. [4] (Big-O-Notation) What is the order of growth of the following functions in Big-o notation? a. f(N) = (N® + 100M2 + 10N + 50) b. f(N) = (10012 + 10N +50) /N2 c. f(N) = 10N + 50Nlog (N) d. f(N) = 50N2log (n)/N
6. Using big-oh notation, give the runtime for each of the following recursive functions. You do not need to justify your answers: a) Int nonesense (int n) if (n <0) return 1; return nonsense (n-2) 1; b) int no nonesense (int n) if (n <0) return 1; return no_nonsense (n-1)+ no nonsense (n-1)
What is the order of the following growth function expressed using Big-Oh notation: T(N)=7*N3 + N/2 + 2 * log N + 38 ? O(2N) O(N3) O(N/2) O(N3 + log N)
Q6) let T(n) be a running time function defined recursively as 0, n=0 n=1 3T(n - 1)- 2T(n - 2), n> 1 a) Find a non-recursive formula for T(n) b) Prove by induction that your answer in part (a) is correct. c) Find a tight bound for T(n).
Choose the equivalent Big Oh notation for the functions given below. If there is more than one option, circle the tightest asymptotic bound. function f(n) = 5n - 10 belongs to a) O(1) b) O(n) c) O(n2) d) O(log n) function f(n) = 4n2 + 4n + 1 belongs to a) O(1) b) O(n) c) O(n2) d) O(log n) function f(n) = n2 + 100 log n belongs to a) O(1) b) O(n) c) O(n2) d) O(log n)
Q-6e: Determine the big-O expression for the following T(N) function: T(1) = 1 T(N) = 2T(N – 1)+1 O 0(1) O O(log N) OO(N2) O O(N log N) O 0(2) OO(N)
A 2 × n checkerboard is to be tiled using three types of
tiles. The first tile is a
white 1 × 1 square tile. The second tile is a red 2 × 2 tile
and the third one is a black 2 × 2 tile.
Let t(n) denote the number of tilings of the 2 × n
checkerboard using white, red and black tiles.
(a) Find a recursive formula for t(n) and use it to determine
t(7).
(b) Let...