{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/revised-note-on-learning-algorithms-for","title":"Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks","arxiv_id":"1706.07450","date":"2017-06-22","proceeding":null,"authors":["Alex Nowak","Soledad Villar","Afonso S. Bandeira","Joan Bruna"],"abstract":"Inverse problems correspond to a certain type of optimization problems\nformulated over appropriate input distributions. Recently, there has been a\ngrowing interest in understanding the computational hardness of these\noptimization problems, not only in the worst case, but in an average-complexity\nsense under this same input distribution.\n  In this revised note, we are interested in studying another aspect of\nhardness, related to the ability to learn how to solve a problem by simply\nobserving a collection of previously solved instances. These 'planted\nsolutions' are used to supervise the training of an appropriate predictive\nmodel that parametrizes a broad class of algorithms, with the hope that the\nresulting model will provide good accuracy-complexity tradeoffs in the average\nsense.\n  We illustrate this setup on the Quadratic Assignment Problem, a fundamental\nproblem in Network Science. We observe that data-driven models based on Graph\nNeural Networks offer intriguingly good performance, even in regimes where\nstandard relaxation based techniques appear to suffer.","url_abs":"http://arxiv.org/abs/1706.07450v2","url_pdf":"http://arxiv.org/pdf/1706.07450v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"revised-note-on-learning-algorithms-for","repo_url":"https://github.com/alexnowakvila/QAP_pt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"revised-note-on-learning-algorithms-for","repo_url":"https://github.com/chaitjo/learning-tsp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"revised-note-on-learning-algorithms-for","repo_url":"https://github.com/longkangli/pfss-il","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.07450","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}