{"type":"ScholarlyArticle","title":"Constraint Satisfaction as Global Optimization","authors":[{"name":"Pedro Meseguer","slug":"pedro-meseguer","given":"Pedro","family":"Meseguer","affiliation":""},{"name":"Javier Larrosa","slug":"javier-larrosa","given":"Javier","family":"Larrosa","affiliation":""}],"abstract":"We present a optimization formulation for discrete binary CSP, based on the construction of a continuous function A(P) whose global maximum represents the best possible solution for that problem. By the best possible solution we mean either (i) a solution of the problem, if it is solvable, or (ii) a partial solution violating a minimal number of constraints, if the problem is unsolvable. This approach is based on relaxation labeling techniques used to enforce consistency in image interpretation. We have used a projected gradient ascent algorithm to maximize A(P) on the n-queens problem obtaining good results but with a high computational cost. To elude this problem, we have developed a heuristic for variable and value selection inspired in the direction in which A(P) is maximized. We have tested this heuristic with forward checking on several classes of CSP.","doi":null,"url":"https://ijcai.org/proceedings/1995/76","pdf":"https://ijcai.org/proceedings/1995/0076.pdf","markdown":"https://ijcai.org/proceedings/1995/76.md","bibtex":"https://ijcai.org/proceedings/1995/bibtex/76","volume":{"slug":"1995","title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1995","url":"https://ijcai.org/proceedings/1995/","year":1995,"location":"Montréal, Québec, Canada","dates":"","editors":"Chris S. Mellish","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Constraint Satisfaction","track":"Constraint Satisfaction","number":76,"pages":{"first":579,"last":584},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}