New Directions in Cylindrical Algebraic Decomposition

Explore new research directions in Cylindrical Algebraic Decomposition (CAD) through this 36-minute lecture by Matthew England from Coventry University, UK. Delve into two key areas of advancement: the development of search-based algorithms utilizing CAD theory for solving non-linear polynomial constraint problems, and the application of machine learning to improve heuristics in CAD computation. Gain insights into how these innovations address the challenge of doubly exponential complexity in CAD, potentially expanding its practical applications. Learn about the integration of CAD with SAT/SMT techniques and discover how machine learning algorithms outperform traditional human-designed heuristics in making critical decisions during CAD computation.

New Directions in Cylindrical Algebraic Decomposition