polynomial factorization Berlekamp algorithm algebraic structures computational methods machine learning hybrid models

Algebra plays a critical role in many mathematical disciplines and real-world applications, with polynomial factorization standing as a central problem. Despite significant progress, challenges remain in streamlining computational methods for various algebraic structures. This study aims to explore and enhance existing techniques for factorizing polynomials, focusing on improving efficiency and accuracy. We employed a comparative analysis of traditional methods such as the Berlekamp algorithm and modern approaches leveraging machine learning models. Our findings indicate that while machine learning techniques offer promising results in specific scenarios, traditional methods still hold significant value in broader applications. The study concludes that a hybrid approach, combining both conventional and novel methodologies, offers the most promise in advancing polynomial factorization capabilities. Future research should focus on refining these hybrid models and exploring their applications in other facets of algebra.

Login to Download PDF

Please login with your Paper ID and password to access the full PDF.

🔑 Login to Download