{"type":"ScholarlyArticle","title":"Learning Restart Strategies","authors":[{"name":"Matteo Gagliolo","slug":"matteo-gagliolo","given":"Matteo","family":"Gagliolo","affiliation":""},{"name":"Jürgen Schmidhuber","slug":"jurgen-schmidhuber","given":"Jürgen","family":"Schmidhuber","affiliation":""}],"abstract":"Restart strategies are commonly used for minimizing the computational cost of randomized algorithms, but require prior knowledge of the run-time distribution in order to be effective. We propose a portfolio of two strategies, one fixed, with a provable bound on performance, the other based on a model of run-time distribution, updated as the two strategies are run on a sequence of problems. Computational resources are allocated probabilistically to the two strategies, based on their performances, using a well-known K-armed bandit problem solver. We present bounds on the performance of the resulting technique, and experiments with a satisfiability problem solver, showing rapid convergence to a near-optimal execution time.","doi":null,"url":"https://ijcai.org/proceedings/2007/127","pdf":"https://ijcai.org/proceedings/2007/0127.pdf","markdown":"https://ijcai.org/proceedings/2007/127.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/127","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":127,"pages":{"first":792,"last":796},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}