Comparison of the time complexity among different sorting algorithms ? (multiple choice) a. Best case of Selection sort is O(n^2) b. Best case of Radix sort is O(nlogn) c. Worst case of Selection sort is O(n^2) Od. Worst case of Merge sort is O(nlogn) e. Best case of Bubble sort is O(n^2) O f. Average case of Shell sort is O(n) O g. Average case of Insertion sort is O(n^2) h. Average case of Quick sort is O(n^2) i. Average case of Merge sort is O(nlogn) j. Worst case of Radix sort is O(n^2) Ok. Best case of Quick sort is O(n) OI. Worst case of Quick sort is O(n^2)

Comparison of the time complexity among different sorting algorithms ? (multiple choice) a. Best case of Selection sort is O(n^2) b. Best case of Radix sort is O(nlogn) c. Worst case of Selection sort is O(n^2) Od. Worst case of Merge sort is O(nlogn) e. Best case of Bubble sort is O(n^2) O f. Average case of Shell sort is O(n) O g. Average case of Insertion sort is O(n^2) h. Average case of Quick sort is O(n^2) i. Average case of Merge sort is O(nlogn) j. Worst case of Radix sort is O(n^2) Ok. Best case of Quick sort is O(n) OI. Worst case of Quick sort is O(n^2)

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

Comparison of the time complexity among different sorting algorithms ? (multiple choice) ^: Indicates exponent operation 單選或多選: ☐a. Worst case of Radix sort is O(n^2) Ob. Best case of Bubble sort is O(n^2) c. Worst case of Quick sort is O(n^2) Od. Best case of Selection sort is O(n^2) ☐e. Best case of Radix sort is O(nlogn) f. Average case of Shell sort is O(n) Og. Worst case of Merge sort is O(nlogn) Oh. Best case of Quick sort is O(n) ☐i. Average case of Insertion sort is O(n^2) O j. Average case of Quick sort is O(n^2) Ok. Average case of Merge sort is O(nlogn) O 1. Worst case of Selection sort is O(n^2)
Merge sort is an efficient sorting algorithm with a time complexity of O(n log n). This means that as the number of elements (chocolates or students) increases significantly, the efficiency of merge sort remains relatively stable compared to other sorting algorithms. Merge sort achieves this efficiency by recursively dividing the input array into smaller sub-arrays, sorting them individually, and then merging them back together. The efficiency of merge sort is primarily determined by its time complexity, which is , where n is the number of elements in the array. This time complexity indicates that the time taken by merge sort grows logarithmically with the size of the input array. Therefore, even as the number of chocolates or students increases significantly, merge sort maintains its relatively efficient performance. Regarding the distribution of a given set of x to y using iterative and recursive functions, the complexity analysis depends on the specific implementation of each…
Question 6: Professor Holmes has come up with a new sorting algorithm. He calls it Trinary Sort and "claims" that it is asymptotically faster than Merge Sort, despite the fact both the algorithms operate using similar logic. But, unlike Merge Sort, Trinary Sort splits the input array into (roughly) 3 equal parts at each step of the recursion as long as the array is splittable (i.e., has at least 3 elements). Trinary Sort's merge subroutine, similar in principle to the one used by Merge Sort, takes 3 sorted subarrays and merges them to produce a single sorted array. Given all of this, answer the following questions. (a) In Merge Sort, the merge subroutine makes n-1 comparisons to merge 2 arrays of size n/2, which takes (n) time. How many comparisons will the merge subroutine of Trinary Sort make to merge 3 arrays of size n/3? What would be the (...) bound on the running time for this subroutine? (b) What is the (...) bound on the running time of the Trinary Sort algorithm? Come up with…
Explain the running time of tree Sort Algorithm if the input dada array is Sorted (ascending) and sorted (descending) and not sorted. Explain the running time of Block Sort Algorithm if the input data array is Sorted (ascending) and Sorted (descending) and not Sorted. 5. Explain the running time of Bucket Sort Algorithm if the input data array is Sorted (ascending) and Sorted (descending) and not Sorted.
AssuAssume we want to analyze empirically 4 variants of the Quicksort algorithm by varying the selection of the pivot and the recursive call as follows: ● Try the following values when selecting a pivot: - Pick the last element as pivot - Pick a random element as pivot ● Do not make a recursive call to QuickSort when the list size falls below a given threshold, and use Insertion Sort to complete the sorting process instead. Try the following values for the threshold size: - Log2(N) - Sqrt(N) Therefore, you are asked: a. (12 points) Write the java code for the 4 Quicksort implementations. b. (5 points) Write a driver program that allows you to measure the running time in milliseconds of the 4 implementations for N = 10000, 20000, 40000, 80000 and 160000. For each data size N, generate a random list of N random integers ranging from 1 to 107 and use the same list to measure the running time of the 4 implementations. Present the results in the following table:me we want to analyze…
Explain the running time of Tim sort Algorithm if the input data array is Sorted (ascending) and Sorted (descending) and not sorted. 2. Explain the running time of tree Sort Algorithm if the input dada array is Sorted (ascending) and sorted (descending) and not sorted. 3. Explain the running time of counting Sort Algorithm if the input data array is Sorted (ascending) and Sorted (descending) and not Sorted. 4. Explain the running time of Block Sort Algorithm if the input data array is Sorted (ascending) and Sorted (descending) and not Sorted. 5. Explain the running time of Bucket Sort Algorithm if the input data array is Sorted (ascending) and Sorted (descending) and not Sorted.
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.
One disadvantage of using a For-loop in sorting algorithms is that the body of theFor-loop is repeatedly executed even if it is no longer necessary. One example isBubble Sort:Input: Array A[1 . . . n]Output: Array A sortedfor i ←1 to (n −1) dofor j ←1 to (n −i) doif A[j] > A[j + 1] thenA[j] ↔A[j + 1]return AIn the array [2, 1, 3, 7, 9] we know that when 2 and 1 are interchanged, early on in theexecution of the algorithm, the array is now sorted, and all subsequent iterations ofthe outer loop are unnecessary. *Write a version of Bubble Sort that avoids unnecessary comparisons*
Sorting a data set is an important sub-problem in data science. Given the size n of a data set, which statements are correct? a) Bubble Sort has worst-case run-time complexity O(n). b) Bubble Sort has worst-case run-time complexity O(n log N). c) Bubble Sort has worst-case run-time complexity O(n²). d) Merge Sort has worst-case run-time complexity O(n). e) Merge Sort has worst-case run-time complexity 0(n log N). f) Merge Sort has worst-case run-time complexity 0(n²). g) Quick Sort has worst-case run-time complexity O(n). h) Quick Sort has worst-case run-time complexity O(n log N). i) Quick Sort has worst-case run-time complexity O(n?).
For each of the following situations, name the best sorting algorithm we studied. (For one or two questions, there may be more than one answer deserving full credit, but you only need to give one answer for each.) (a) The array is mostly sorted already (a few elements are in the wrong place). (b) You need an O(n log n) sort even in the worst case and you cannot use any extra space except for a few local variables. (c) The data to be sorted is too big to fit in memory, so most of it is on disk. (d) You have many data sets to sort separately, and each one has only around 10 elements. (e) You have a large data set, but all the data has only one of about 10 values for sorting purposes (e.g., the data is records of elementary-school students and the sort is by age in years). (f) Instead of sorting the entire data set, you only need the k smallest elements where k is an input to the algorithm but is likely to be much smaller than the size of the entire data set
Sorting a data set is an important sub-problem in data science. Given the size n of a data set, whichstatements are correct?a) Bubble Sort has worst-case run-time complexity O(n).b) Bubble Sort has worst-case run-time complexity O(n log N).c) Bubble Sort has worst-case run-time complexity O(n2).d) Merge Sort has worst-case run-time complexity O(n).e) Merge Sort has worst-case run-time complexity O(n log N).f) Merge Sort has worst-case run-time complexity O(n2).g) Quick Sort has worst-case run-time complexity O(n).h) Quick Sort has worst-case run-time complexity O(n log N).i) Quick Sort has worst-case run-time complexity O(n2).
Write a sorting algorithm that sorts a list of integer numbers using the bubble sort algorithm. Explain the time complexity of the bubble sort algorithm step by step and then the worst-case time complexity of bubble sort in Big-O notation.