To enhance the success rates of handling more complex polynomial MBA expressions, the E-Graph-based approach can be extended in several ways:
Advanced Rule Set: Introduce more sophisticated rewriting rules specifically tailored for polynomial expressions. By incorporating rules that target the unique characteristics of polynomial MBA expressions, such as nested terms and complex coefficients, the simplification process can be more effective.
Machine Learning Integration: Utilize machine learning techniques to identify patterns in polynomial MBA expressions that can guide the simplification process. By training models on a diverse set of polynomial expressions and their simplified forms, the system can learn to recognize optimal simplification strategies for different types of polynomials.
Constant Folding Optimization: Implement constant folding techniques to reduce the number of constant values in polynomial expressions. By identifying and folding constants within the expressions, the complexity can be significantly reduced, leading to more successful simplifications.
Parallel Processing: Employ parallel processing capabilities to handle the computational load of simplifying complex polynomial expressions. By distributing the workload across multiple cores or machines, the system can expedite the simplification process and improve overall success rates.