Match appropriate worst case time complexities Selection sort Merge sort Quick sort Binary search [Choose ] [Choose ] [Choose ] ✓ [Choose ] O(logN) O(NlogN) ΟΙΝΛΟΙ

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
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**Exercise: Match Appropriate Worst Case Time Complexities**

Below are various algorithms. Your task is to match each algorithm with its correct worst-case time complexity. Use the dropdowns to select the correct option for each.

1. **Selection Sort**
   - [ Choose ]

2. **Merge Sort**
   - [ Choose ]

3. **Quick Sort**
   - [ Choose ]

4. **Binary Search**
   - [ Choose ]  
     Options: 
     - O(logN)
     - O(NlogN)
     - O(N^2)

**Instructions:**
- Selection Sort typically has a worst-case time complexity of O(N^2).
- Merge Sort's worst-case time complexity is O(NlogN).
- Quick Sort, in the worst case, can take O(N^2) time, though its average case is O(NlogN).
- Binary Search has a worst-case time complexity of O(logN).
Transcribed Image Text:**Exercise: Match Appropriate Worst Case Time Complexities** Below are various algorithms. Your task is to match each algorithm with its correct worst-case time complexity. Use the dropdowns to select the correct option for each. 1. **Selection Sort** - [ Choose ] 2. **Merge Sort** - [ Choose ] 3. **Quick Sort** - [ Choose ] 4. **Binary Search** - [ Choose ] Options: - O(logN) - O(NlogN) - O(N^2) **Instructions:** - Selection Sort typically has a worst-case time complexity of O(N^2). - Merge Sort's worst-case time complexity is O(NlogN). - Quick Sort, in the worst case, can take O(N^2) time, though its average case is O(NlogN). - Binary Search has a worst-case time complexity of O(logN).
Expert Solution
Step 1: Introduce worst-case time complexity:

Worst-case time complexity is a measure of an algorithm's maximum running time under the least favorable conditions. It represents the upper bound on the number of basic operations an algorithm could perform when given a specific input. It is a crucial metric for understanding an algorithm's performance in the worst possible scenario.

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