Pseudo Boolean

Pseudo-Boolean (PB) formulas offer a more concise and expressive representation of Boolean problems than traditional conjunctive normal form (CNF), leading to increased research interest in their applications. Current research focuses on developing efficient algorithms for weighted model counting and exact model counting of PB formulas, often employing techniques like dynamic programming and knowledge compilation using algebraic decision diagrams. This work is significant because PB formulas' enhanced expressiveness enables more efficient solutions for various problems, including image processing (edge detection and segmentation) and data analysis (dimensionality reduction and clustering). The development of improved PB solvers promises advancements in diverse fields requiring efficient Boolean reasoning.