{"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/scaling-submodular-optimization-approaches","title":"Scaling Submodular Optimization Approaches for Control Applications in Networked Systems","arxiv_id":"1810.02837","date":"2018-10-05","proceeding":null,"authors":["Arun V. Sathanur"],"abstract":"Often times, in many design problems, there is a need to select a small set\nof informative or representative elements from a large ground set of entities\nin an optimal fashion. Submodular optimization that provides for a formal way\nto solve such problems, has recently received significant attention from the\ncontrols community where such subset selection problems are abound. However,\nscaling these approaches to large systems can be challenging because of the\nhigh computational complexity of the overall flow, in-part due to the\nhigh-complexity compute-oracles used to determine the objective function\nvalues. In this work, we explore a well-known paradigm, namely leader-selection\nin a multi-agent networked environment to illustrate strategies for scalable\nsubmodular optimization. We study the performance of the state-of-the-art\nstochastic and distributed greedy algorithms as well as explore techniques that\naccelerate the computation oracles within the optimization loop. We finally\npresent results combining accelerated greedy algorithms with accelerated\ncomputation oracles and demonstrate significant speedups with little loss of\noptimality when compared to the baseline ordinary greedy algorithm.","url_abs":"http://arxiv.org/abs/1810.02837v1","url_pdf":"http://arxiv.org/pdf/1810.02837v1.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":"scaling-submodular-optimization-approaches","repo_url":"https://github.com/arunsv/submodular","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}