The GLR Framework: A Novel Algorithmic Approach to NP Problems

Abstract In this paper, I introduce the Guided Local Reduction (GLR) framework, a novel approach to tackling NP-complete problems. GLR integrates principles from local search, reduction techniques, and dynamic heuristic guidance to address computationally hard problems such as SAT and constraint satisfaction. I define the framework, explain its components, and demonstrate its application with a Sudoku-to-SAT encoding example. I also reflect on implications for the P versus NP question and outline future directions.