KunjMaheshwari
commented
1 week ago I’m excited to submit my first-ever open-source contribution for Hacktoberfest, which includes implementations of key Java sorting algorithms with clean code, detailed documentation, and tests. I’d be grateful if you could review and merge my PR, and I’m open to any feedback for improvement.
Please can you add Hactoberfest labels in my pull request so that It will be accepted on the Hactoberfest Profile. @subin-shk
🚀 Greedy Algorithms in Java This PR introduces a set of Greedy Algorithms implemented in Java, designed to solve optimization problems by making locally optimal choices at each step. The following algorithms have been implemented, with a focus on clarity, efficiency, and scalability:
Activity Selection Problem: Determines the maximum number of activities that can be performed without overlapping, using a sorted approach. Fractional Knapsack Problem: Implements the fractional knapsack solution by maximizing the value-to-weight ratio. Huffman Encoding: Constructs an optimal prefix code using greedy techniques to minimize the average encoding length. Job Sequencing Problem: Finds the optimal job scheduling to maximize profit when each job has a deadline. Minimum Coins Problem: Provides a solution to determine the minimum number of coins required to make a specific amount of change.
📝 Key Highlights: Efficient Algorithms: Time complexity has been minimized where possible, leveraging sorting techniques and heap-based data structures for performance. Modular Code: Each algorithm is implemented as an independent module for ease of testing and integration. Extensive Documentation: Inline comments and Javadoc annotations have been added to explain the logic, assumptions, and edge case handling for each algorithm. Test Cases: Comprehensive test cases included to validate different scenarios, from basic functionality to edge cases.
💡 Why Greedy Algorithms? Greedy algorithms are a powerful tool for solving optimization problems, where the local optimal solutions lead to a global optimum. This PR adds foundational algorithms that can be extended and modified for more complex use cases in various domains like scheduling, compression, and resource allocation.