Suppose we have the following functions: n log n, n² log n, logn Which order of the functions so that each function is big-O of the next function. ... (1) log n < n log n < n² log n (2) n² log n < log n < n log n (3) n log n < log n < n² log n (4) log n < n² log n < n log n A Choice (3) (B Choice (1) Choice (2) D Choice (4)

Suppose we have the following functions: n log n, n² log n, logn Which order of the functions so that each function is big-O of the next function. ... (1) log n < n log n < n² log n (2) n² log n < log n < n log n (3) n log n < log n < n² log n (4) log n < n² log n < n log n A Choice (3) (B Choice (1) Choice (2) D Choice (4)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Identify the big-O time complexity of each of the following functions:void f1(int n) {for(int i=0; i<(3*n+n); ++i) {cout << i << endl;}for(int i=0; i<(10*n); ++i) {cout << i << endl;}}void f2(int n) {if(n==0) { return; }f2(n-1);f2(n-3);}void f3(int n) {for(int i=0; i<n; ++i) {for(int j=0; j<n; ++j) {for(int k=0; k<n; ++k) {cout << i << endl;}}}}
Question 4: (15 Marks) Find the running times of each of the following functions (in big-O notation), and arrange them in increasing complexity. [Note: your answer should provide a detailed explanation that support your final conclusion] a) T(n) = ET(i) b) T(n) = 6 T( n /2 )+ O( n) c) T(n) = T(n - 2) + log n with T(0) = 1 | Note: You should submit all your answers for the above-mentioned questions in one big word file (one after the other). The file should contain the following items for each question: Criterion Unsatisfactory Average Good O 100% Complete and in a professional format O Explanation of the pseudo-code is provided O looks efficient in terms of running time and of Algorithm Design O Incomplete. O Complete but looks like an essay not a professional algorithm. memory usage O Comments on the efficiency O Organized work, Implementation Using Python Incomplete Completed 100% O Comments on the code Poor use of white with correct use of space, variables, Screen shot of the run…
Answer (b) and the other subparts please.
def f(A, B): 8 + 1 if A <= 0: } return B else: return f(A-1, B) double f(int A, int B) { if (A <= 0) { return B; } else { return f(A-1, B); What is the time complexity of the function ()? Select one: OA 0(2^A) OB. O(A^2) OC. O(A log 2 A) OD. None of the others OE O(A)
INPUT:5 847864OUTPUT:4
(1) Next try writing a simple function euler_phi to compute Euler's function. This should take an integer n, and for each i from 1 to n − 1, adds one to a counter each time we have gcd(i, n) = 1 (i.e., for each i which is relatively prime to n). Is this code quicker than semiprime_factorisation1? That is, if we wanted to compute (n) for n semiprime, is it quicker to use euler_phi directly, or to use semiprime_factorisation and then calculate (n) = (p − 1)(q − 1)? (2) It's not too hard to see that a faster semiprime factorisation function is trial division, also called direct search factorisation. This simply involves dividing n by each integer from 2 to [√n], to find integers which divide n exactly (that is, have a remainder of zero). Write a function semiprime_factorisation2 which takes an integer n, which is a semiprime (i.e., is the product of two primes p and q), and returns both p and q. Remember to use integer division here, with //. Test your function on some values of n = pq.…
Let T(n) be a function obeying the recurrence T(n)=5T(n/5) +1, then T(n)= Select one: a. \( \theta(logn *n) \) b. \(\theta(1) \) c. none O d. \(\theta(n) \)
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Find the time complexity of the functions shown below with steps
Find the upper bound of the function given bellow 1, n<1 T(n) = %3D +c, n2 1
GROWTH OF FUNCTIONS. Arrange the following mathematical terms from lowest to highest order. n3 n2 n! 2n 7n – 2 log n n log n 10n n5 + log n n2 + n log n Example: a < b < c < d < … < x < y < z