Question 1 Which of the following is not a comparison-based sorting algorithm? Insertion Sort Merge Sort О Наp Sort O Counting Sort Question 2 What is time complexity of fun()? int fun(int n) { int count = 0; for (int i = n; i > 0; i /= 2) for (int j %3D = 0; j < i; j++) %3D count += 1; return count; } O(Log n) O On^2) O(n) Oln^0.5) O On Log n)

Question 1 Which of the following is not a comparison-based sorting algorithm? Insertion Sort Merge Sort О Наp Sort O Counting Sort Question 2 What is time complexity of fun()? int fun(int n) { int count = 0; for (int i = n; i > 0; i /= 2) for (int j %3D = 0; j < i; j++) %3D count += 1; return count; } O(Log n) O On^2) O(n) Oln^0.5) O On Log n)

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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Below are a number of statements about sorting algorithms. Which of the above statements is correct? A. Insertion location and Mergesort are examples of comparison-based sorting algorithms.B. Quicksort is an example of the "divide-and-conquer" algorithm technique.C. Mergesort is a sorting algorithm which in both worst and average case has T (n) = 0 (n log2 n).D. Heapsort has a time complexity in the worst case that is better (faster) than the time complexity of Quicksort in the worst case.E. Selection type has the time complexity T (n) = O (n log2 n) in the worst case.F. Radixsort is not a comparison-based sorting algorithm. Group of answer options All statements are correct. No statement is correct. All statements except D are correct. All statements except E are correct. Only statements A, B, C and D are correct. Only statements A, B and F are correct.
Please explai Which algorithm has a better Big-O complexity heapsort or merge sort? Group of answer choices a. Merge Sort b. Heap sort c. They have equal Big-O complexities
Suppose you are given the following array: {23, 48, 35, 12, 37, 36, 24, 49, 10} Perform the heapify algorithm on the complete binary tree represented by the above array to form a maxheap.(bigger number has higher priority). Provide the final heap created by the heapify in array form. If you wish to add intermediate steps in array form you are welcome to do so. If you do so please clearly state which array is your final answer
In this problem, consider a non-standard sorting algorithm called the Slow Sort. Given anarray A[1 : n] of n integers, the algorithm is as follows: Slow-Sort(A[1 : n]):1. If n < 100, run merge sort (or selection sort or insertion sort) on A.2. Otherwise, run Slow-Sort(A[1 : n −1]), Slow-Sort(A[2 : n]), and Slow-Sort(A[1 : n −1]) again. Question: Write a recurrence for Slow-Sort and use the recursion tree method to solve this recurrenceand find the tightest asymptotic upper bound on the runtime of Slow-Sort.
Consider a hybrid sorting algorithm that combines Mergesort with Insertion Sort.It uses Mergesort until the number of elements in the input becomes smaller than or equal to 8, after which it switches to Insertion Sort. What is the number of key comparisons performed by this hybrid sorting algorithm in the best case when running on an input array of size n? Briefly justify your answer. You could assume n = 2k, k is more than 3
Anwer the three questions 1. Which of the following statements are correct? (Select all that applies.) Group of answer choices a. Merge-sort and quick-sort algorithms are based on the divide-and-conquer paradigm. b. The time complexity of selection-sort algorithm is O(n^2). c. The time complexity of insertion-sort algorithm is O(n^2). d. The time complexity of heap-sort and merge sort algorithms is O(nlogn). e. Expected running time of quick-sort algorithm is O(nlogn). 2. The key process in quick-sort is the partition. The target of partitions is, given an array and an element x of an array as the pivot, put x at its correct position in a sorted array and put all smaller elements (smaller than x) before x, and put all greater elements (greater than x) after x. Group of answer choices a. True b. False 3. Which one is an example of not-in-place sorting? Group of answer choices a. Selection-sort b. Insertion-sort c. Merge-sort d. Quick-sort
A sequential search of a sorted list can halt when the target is less than a givenelement in the list. Define a modified version of this algorithm and state thecomputational complexity, using big-O notation, of its best-, worst-, and average-case performances. Using this template in python: def sequential_search(input_list, target):target_index = -1#TODO: Your work here# Return target_index. If not found, -1return target_indexif __name__ == "__main__":my_list = [1, 2, 3, 4, 5]print(sequential_search(my_list, 3)) # Correct Output: 2print(sequential_search(my_list, 0)) # Correct Output: -1
The maximum (or minimum) item in a heap is always located at its root. A sorting algorithm called Heap Sort uses this data structure to sort a list in O(n log n) time. How would this algorithm look like? Provide a pseudocode, flowchart, or actual code (in Python) and show it is able to achieve this time complexity. What is the cost to achieve this faster time complexity?
Mergesort and Heapsort are often referred to as optimal time sortingalgorithms but the same cannot be said about insertion sort. Explainbriefly why that is so.
As described in Section 21.5 of your textbook, it is possible to make the heap sort algorithm more efficient by writing a method that will build a heap in place using the array to be sorted. Implement such a method and rewrite the heap sort algorithm to make use of it. Show test cases for various inputs, including sorted and unsorted inputs.
Given an array of positive and negative integers, write the main ideas of an algorithm to segregate them without changing the relative order of elements. The output should contain all positive numbers follow negative numbers while maintaining the same relative ordering. Example: Input: [9, -3, 5, -2, -8, -6, 1, 3] Output: [-3, -2, -8, -6, 9, 5, 1, 3] Determine the running time complexity of this algorithm
Computer Science whrite down the recurrence relationship(Functions) of the following Algorithms for their Worst, Best and Average cases and slove them by using subtitution Method: 1.Quick sort 2.Selection sort 3.Heap Sort 4. Insertion sort 5.Bubble sort 6. Radix Sort 7.Merge Sort 8.Conting Sort