Consider the following recursive algorithm. Algorithm Mystery A[0..n-1]) //Input: An array A[0..n - 1] of real numbers if n = 1 return A[0] else temp← Riddle(A[0..n -2]) if temp ≤ A[n 1] return temp else return A[n - 1] a. What does this algorithm compute? b. What is its input size? c. What is its basic operation? d. Set up a recurrence relation for the algorithm's basic operation count and solve it. e. What is the efficiency class of this algorithm?

Consider the following recursive algorithm. Algorithm Mystery A[0..n-1]) //Input: An array A[0..n - 1] of real numbers if n = 1 return A[0] else temp← Riddle(A[0..n -2]) if temp ≤ A[n 1] return temp else return A[n - 1] a. What does this algorithm compute? b. What is its input size? c. What is its basic operation? d. Set up a recurrence relation for the algorithm's basic operation count and solve it. e. What is the efficiency class of this algorithm?

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

See similar textbooks

Similar questions

You want to design an algorithm, called minMax(A,p,r), that takes an array of integers and indexes of the first and last elements, and returns the minimum and maximum values in that range. Now write the pseudo code of a divide and conquer (and therefore, recursive) algorithm with the same time complexity (Θ(n)). You can assume that p ≤ r. Also, in your code, you can return two numbers by returning a pair, e.g. “return (a, b)”, and can save the output in a similar way, e.g. “(a, b) = minMax(parameters)”. (Short answer please)
Implement the algorithm in C++. Algorithm: substring (s, p, len) Initialize i = p and count =0 Use an array „dest‟ to hold the required substring Repeat steps 4 ,5 and 6 while count <len dest[count] =s[i] count = count+1 i = i +1 Insert string terminator at end of dest Exit
Consider the following recursive algorithm. ALGORITHM Riddle(A[0..n–1]) //Input:AnarrayA[0..n –1]of real numbers if n =1 returnA[0] else tempe-Riddle(A[0..n–2]) if temp < A[n-1] return temp else returnA[n-1] a. What does this algorithm compute? b. Set up a recurrence relation for the algorithm's basic operation count and solve it.
In a sequence, numbers decrease up to a point and then increase. Example: A=[10,9,8,7,6,5,13,14,15] Write an algorithm with O(logn) time complexity that finds the smallest element in such an array. (can be pseudocode.)
the task is to find the Kth smallest element in the given array..
Write the algorithm which finds the second maximum value of the array. Then find the complexity of the algorithm as Big O notation
Consider the algorithm given below. ALGORITHM MinDistance(A[0..n − 1]) //Input: Array A[0..n − 1] of numbers //Output: Minimum distance between two of its elements dmin←∞ for i ←0 to n − 1 do for j ←0 to n − 1 do if i != j and |A[i]− A[j ]| < dmin dmin ←|A[i]− A[j ]| return dminif n = 20, and the list is sorted in descending order, then the loop " or i ←0 to n − 1 do " will be executed ______ times.
Given n arrays, each array contain n positive integers. Write an O (n² log n) algorithm to find the smallest n sums out of nn possible sums that can be obtained by picking one positive integer from each of n arrays. For example, given three arrays as follows: [5, 1, 8], [5, 2,9], and [6,7, 10]. The smallest n sums of the given array is [9, 10, 12].
Consider the Insertion Sort Function below:public static void insertionSort(int array[]){ int n = array.length; for (int j = 1; j < n; j++) { int key = array[j]; int i = j-1; while ( (i > -1) && ( array [i] > key ) ) { array [i+1] = array [i]; i--; } array[i+1] = key;} }Discuss when is its best case, and worst case performance.
Question # 02: Let’s assume we want to find the largest number in a non-empty array A. A sample solution can be following: def max(A): answer = A[0] for j in range(1,len(A)): // in pseudo-code: for i=1,...,len(A)-1 if (A[j]>answer): answer = A[j] return answer Perform the code correction check including the loop invariance. Question # 03: illustrate the operation of merge sort on the array A = {3; 41; 52; 26; 38; 57; 9; 49}. Question # 04: Answer the following short questions: a) Is 2n+1 = O(2n)? b) Is 22n = O(2n)? c) f(n)=O(g(n)) implies g(n)=O(f(n)) d) f(n) = O((f(n))2)