A Lagrangian reconstruction of GENET

GENET is a heuristic repair algorithm which demonstrates impressive e ciency in solving some large-scale and hard instances of constraint satisfaction problems (CSPs). In this paper, we draw a surprising connection between GENET and discrete Lagrange multiplier methods. Based on the work of Wah and Shang, we propose a discrete Lagrangian-based search scheme LSDL, de ning a class of search algorithms for solving CSPs. We show how GENET can be reconstructed from LSDL. The dual viewpoint of GENET as a heuristic repair method and a discrete Lagrange multiplier method allows us to investigate variants of GENET from both perspectives. Benchmarking results con rm that rst, our reconstructed GENET has the same fast convergence behavior as the original GENET implementation, and has competitive performance with other local search solvers DLM, WalkSAT, and Wsat(oip), on a set of di cult benchmark problems. Second, our improved variant, which combines techniques from heuristic repair and discrete...

• Type: Journal
• Year: 2000
• Where: AI
• Authors: Kenneth M. F. Choi, Jimmy Ho-Man Lee, Peter J. Stuckey
• Keywords: AI 2000, Artificial Intelligence, Constraint Satisfaction Problems, Heuristic Repair, Lagrange Multiplier Method