{"type":"ScholarlyArticle","title":"Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem: Application to Materials Discovery","authors":[{"name":"Yexiang Xue","slug":"yexiang-xue","given":"Yexiang","family":"Xue","affiliation":""},{"name":"Stefano Ermon","slug":"stefano-ermon","given":"Stefano","family":"Ermon","affiliation":""},{"name":"Carla P. Gomes","slug":"carla-p-gomes","given":"Carla P.","family":"Gomes","affiliation":""},{"name":"Bart Selman","slug":"bart-selman","given":"Bart","family":"Selman","affiliation":""}],"abstract":"Exploiting parallelism is a key strategy for speeding up computation. However, on hard combinatorial problems, such a strategy has been surprisingly challenging due to the intricate variable interactions.We introduce a novel way in which parallelism can be used to exploit hidden structure of hard combinatorial problems. Our approach complements divide-and-conquer and portfolio approaches. We evaluate our approach on the minimum set basis problem: a core combinatorial problem with a range of applications in optimization, machine learning, and system security. We also highlight a novel sustainability related application, concerning the discovery of new materials for renewable energy sources such as improved fuel cell catalysts. In our approach, a large number of smaller sub-problems are identified and solved concurrently. We then aggregate the information from those solutions, and use this information to initialize the search of a global, complete solver. We show that this strategy leads to a substantial speed-up over a sequential approach, since the aggregated sub-problem solution information often provides key structural insights to the complete solver. Our approach also greatly outperforms state-of-the-art incomplete solvers in terms of solution quality. Our work opens up a novel angle for using parallelism to solve hard combinatorial problems.","doi":null,"url":"https://ijcai.org/proceedings/2015/28","pdf":"https://ijcai.org/proceedings/2015/0028.pdf","markdown":"https://ijcai.org/proceedings/2015/28.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/28","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Agent and Multiagent Systems","track":"Main Track","number":28,"pages":{"first":146,"last":154},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}