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https://github.com/muhammadsaadhsn/vertexorderingoptimization
This project implements search algorithms (BFS, DFS, UCS, and Greedy) to find the optimal ordering of vertices in a network, minimizing the total cost. It reads data files with vertices and associated costs, calculates the cost of different orderings, and outputs the optimal ordering and its cost.
https://github.com/muhammadsaadhsn/vertexorderingoptimization
bfs dfs greedy jupternotebook pyhon ucs
Last synced: 2 days ago
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This project implements search algorithms (BFS, DFS, UCS, and Greedy) to find the optimal ordering of vertices in a network, minimizing the total cost. It reads data files with vertices and associated costs, calculates the cost of different orderings, and outputs the optimal ordering and its cost.
- Host: GitHub
- URL: https://github.com/muhammadsaadhsn/vertexorderingoptimization
- Owner: muhammadsaadhsn
- License: mit
- Created: 2024-06-28T06:37:55.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-06-28T06:50:42.000Z (8 months ago)
- Last Synced: 2025-01-28T15:39:50.935Z (14 days ago)
- Topics: bfs, dfs, greedy, jupternotebook, pyhon, ucs
- Language: Jupyter Notebook
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Vertex Ordering Optimization
This repository contains the implementation of various search algorithms (BFS, DFS, UCS, and Greedy) to find the optimal ordering of vertices in a network, minimizing the total cost of the network. This project is inspired by the optimization problem commonly found in Bayesian Network Learning.
## Table of Contents
- [Introduction](#introduction)
- [Data Format](#data-format)
- [Usage](#usage)
- [Algorithms](#algorithms)
- [Example](#example)
- [License](#license)
## Introduction
The goal of this project is to determine a vertex ordering with the minimum cost given a set of vertices and their possible parent sets with associated costs. The problem can be simplified as finding a vertex ordering that minimizes the total cost of the network.
## Data Format
The input data is provided in text files where each line represents a vertex, its parent set, and the associated cost. The format of each line is as follows:
```
vertex, {parent_set}, cost
```
Example:
```
1,{},153.466
1,{3},96.093
1,{4},97.913
1,{5},99.835
2,{},141.023
...
```
## Usage
To run the search algorithms on the provided datasets, execute the following command:
```
python VertexOrderingOptimization.py
```
## Algorithms
### Breadth-First Search (BFS)
BFS explores all possible orderings level by level and finds the minimum cost ordering by visiting each vertex exactly once.
### Depth-First Search (DFS)
DFS explores each possible ordering path to its depth, backtracking when a complete ordering is found, and finds the minimum cost ordering.
### Uniform Cost Search (UCS)
UCS explores the search space similar to BFS but prioritizes paths with lower costs using a priority queue.
### Greedy Search
Greedy search selects the next vertex that leads to the lowest cost incrementally until all vertices are ordered.
## Example
Consider the ordering (5, 3, 1, 4, 2). With respect to vertex 1, the parent set {4} is not consistent with the ordering. The parent sets {}, {3}, and {5} are consistent with the ordering. The ordering (5, 3, 1, 4, 2) has a total cost of 465.435.
### Running the Example
1. Place your data files (e.g., `data0.txt`, `data1.txt`, `data2.txt`) in the project directory.
2. Execute the script to process the files and find the optimal ordering:
```
python main.py
```
3. The script will output the orderings and their costs for each dataset.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request with your improvements.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.