https://github.com/yevheniidatsenko/goit-algo2-hw-10
🗒️ Home Task - Design and Analysis of Algorithms (Algorithmic Complexity, Approximate and Randomized Algorithms)
https://github.com/yevheniidatsenko/goit-algo2-hw-10
deterministic-quicksort goit-algo2-hw-10 python quicksort-algorithm randomised-quicksort
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🗒️ Home Task - Design and Analysis of Algorithms (Algorithmic Complexity, Approximate and Randomized Algorithms)
- Host: GitHub
- URL: https://github.com/yevheniidatsenko/goit-algo2-hw-10
- Owner: yevheniidatsenko
- Created: 2025-03-13T13:57:01.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-13T17:09:46.000Z (3 months ago)
- Last Synced: 2025-03-13T18:28:34.674Z (3 months ago)
- Topics: deterministic-quicksort, goit-algo2-hw-10, python, quicksort-algorithm, randomised-quicksort
- Language: Python
- Homepage:
- Size: 215 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Homework Module: Algorithmic Complexity, Approximate and Randomized Algorithms
## Overview
This homework model focuses on implementing and comparing algorithms related to algorithmic complexity and greedy algorithms. It consists of two main tasks:
1. **QuickSort Comparison**: Implementing and comparing the performance of randomized and deterministic QuickSort algorithms.
2. **Class Schedule Creation**: Using a greedy algorithm to create a class schedule that minimizes the number of teachers required to cover all subjects.
## Tasks Description
### Task 1: QuickSort Comparison
- **Objective**: Implement randomized and deterministic QuickSort algorithms and compare their performance on arrays of different sizes.
- **Requirements**:
- Implement both algorithms.
- Measure and compare their execution times on arrays of varying sizes.
- Visualize the results using a graph.
### Task 2: Class Schedule Creation
- **Objective**: Create a class schedule using a greedy algorithm to minimize the number of teachers needed to cover all subjects.
- **Requirements**:
- Implement a greedy algorithm to assign teachers to subjects.
- Ensure all subjects are covered if possible.
- Display the schedule and indicate if any subjects cannot be covered.
## Results
### Task 1: QuickSort Comparison
- **Execution Times**: The execution times for both algorithms were measured and compared across different array sizes.
- **Visualization**: A graph was created to visualize the performance comparison.
- **Conclusion**: The randomized QuickSort generally performed better than the deterministic version, especially on larger datasets.
### Task 2: Class Schedule Creation
- **Schedule**: A class schedule was successfully created using the greedy algorithm.
- **Coverage**: All subjects were covered by the assigned teachers.
- **Efficiency**: The algorithm minimized the number of teachers required to cover all subjects.
