This repository contains Python implementations of three algorithms for solving the knapsack problem:

- Greedy algorithm
- Dynamic programming algorithm
- Recursive memoization algorithm

It also includes test cases to validate the output of each algorithm using the unittest library.

• Ibrahim
• Reza Hatta Pratama
• Ryan Rafael

The knapsack problem is a classic combinatorial optimization problem. It involves choosing a subset of items, each with a weight and a value, to maximize the total value while ensuring that the total weight does not exceed a given capacity.

The greedy algorithm takes items in decreasing order of value-to-weight ratio until the knapsack is full. It's a simple and fast approach, but it doesn't always produce the optimal solution.

The dynamic programming algorithm builds a table of solutions for subproblems and uses it to find the optimal solution for the full problem. It guarantees an optimal solution but has higher computational complexity.

The recursive memoization algorithm uses recursion to explore possible solutions while storing intermediate results to avoid redundant calculations. It's similar to dynamic programming but uses a top-down approach.

1. Clone this repository:

git clone https://github.com/your-username/knapsack-solver.git

2. Install the required dependency:

pip install unittest

3. Run the test cases:

python -m unittest tests.py

Feel free to contribute to this repository by:

• Reporting issues
• Suggesting improvements
• Submitting pull requests

This project is licensed under the MIT License. See the LICENSE file for details.