What is the process to create an explicit bigO calculator for selection sort, insertion sort merge sort, and quicksort? Java. AP Computer Science A. One class. There is supposed to be four original lists, which is a random list of numbers from 1-10 with 10 items, these lists will be used by the four sorts. There should be the sorted list for those original lists from each sort method. There should be an amount of comparisons for each sort. Also there should be bigO information, the best case, average case, worst case, the nlog2n, the n^2, sorting 10 lists from the sort methods of n increasing. Thank you. What the output can look like:
What is the process to create an explicit bigO calculator for selection sort, insertion sort merge sort, and quicksort? Java. AP Computer Science A. One class. There is supposed to be four original lists, which is a random list of numbers from 1-10 with 10 items, these lists will be used by the four sorts. There should be the sorted list for those original lists from each sort method. There should be an amount of comparisons for each sort. Also there should be bigO information, the best case, average case, worst case, the nlog2n, the n^2, sorting 10 lists from the sort methods of n increasing. Thank you. What the output can look like:
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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What is the process to create an explicit bigO calculator for selection sort, insertion sort merge sort, and quicksort? Java. AP Computer Science A. One class. There is supposed to be four original lists, which is a random list of numbers from 1-10 with 10 items, these lists will be used by the four sorts. There should be the sorted list for those original lists from each sort method. There should be an amount of comparisons for each sort. Also there should be bigO information, the best case, average case, worst case, the nlog2n, the n^2, sorting 10 lists from the sort methods of n increasing. Thank you.
What the output can look like:
![**Educational Content on Sorting Algorithm Analysis**
---
**Initial Data Set:**
- **Original List:** [9, 8, 10, 5, 4, 10, 2, 2, 7, 8]
- **Number of Comparisons:** 36
- **Sorted List Result:** [2, 2, 4, 5, 7, 8, 8, 9, 10, 10]
**Further Sorting Comparisons:**
- **Sorted List:** [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
- **Comparisons Made:** 9
- **Reverse Sorted List:** [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
- **Comparisons Made:** 54
---
**Performance Analysis on Multiple Lists:**
**Sorting 10 Lists:**
- **Size \(n = 10\)**
- **Best:** 23 comparisons
- **Average:** 30 comparisons
- **Worst:** 39 comparisons
- **\((n \log_2 n) = 33\), \(n^2 = 100\)**
- **Size \(n = 100\)**
- **Best:** 2304 comparisons
- **Average:** 2469 comparisons
- **Worst:** 2595 comparisons
- **\((n \log_2 n) = 664\), \(n^2 = 10,000\)**
- **Size \(n = 1000\)**
- **Best:** 243,675 comparisons
- **Average:** 249,917 comparisons
- **Worst:** 258,071 comparisons
- **\((n \log_2 n) = 9966\), \(n^2 = 1,000,000\)**
---
**Explanation:**
This analysis demonstrates the complexity of a sorting algorithm in various scenarios. The number of comparisons provides insight into the efficiency of the algorithm. As the size of the list (n) increases, the number of comparisons grows, illustrating the theoretical time complexities of \(O(n)\), \(O(n \log n)\), and \(O(n^2)\). The best,](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F332efb1b-0950-4ab6-9245-933713b3908a%2Fa51159dd-3cf2-4c33-9174-37842ce710fe%2Fm77gh2l_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Educational Content on Sorting Algorithm Analysis**
---
**Initial Data Set:**
- **Original List:** [9, 8, 10, 5, 4, 10, 2, 2, 7, 8]
- **Number of Comparisons:** 36
- **Sorted List Result:** [2, 2, 4, 5, 7, 8, 8, 9, 10, 10]
**Further Sorting Comparisons:**
- **Sorted List:** [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
- **Comparisons Made:** 9
- **Reverse Sorted List:** [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
- **Comparisons Made:** 54
---
**Performance Analysis on Multiple Lists:**
**Sorting 10 Lists:**
- **Size \(n = 10\)**
- **Best:** 23 comparisons
- **Average:** 30 comparisons
- **Worst:** 39 comparisons
- **\((n \log_2 n) = 33\), \(n^2 = 100\)**
- **Size \(n = 100\)**
- **Best:** 2304 comparisons
- **Average:** 2469 comparisons
- **Worst:** 2595 comparisons
- **\((n \log_2 n) = 664\), \(n^2 = 10,000\)**
- **Size \(n = 1000\)**
- **Best:** 243,675 comparisons
- **Average:** 249,917 comparisons
- **Worst:** 258,071 comparisons
- **\((n \log_2 n) = 9966\), \(n^2 = 1,000,000\)**
---
**Explanation:**
This analysis demonstrates the complexity of a sorting algorithm in various scenarios. The number of comparisons provides insight into the efficiency of the algorithm. As the size of the list (n) increases, the number of comparisons grows, illustrating the theoretical time complexities of \(O(n)\), \(O(n \log n)\), and \(O(n^2)\). The best,
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