{"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/sdp-relaxation-with-randomized-rounding-for","title":"SDP Relaxation with Randomized Rounding for Energy Disaggregation","arxiv_id":"1610.09491","date":"2016-10-29","proceeding":"NeurIPS 2016 12","authors":["Kiarash Shaloudegi","András György","Csaba Szepesvári","Wilsun Xu"],"abstract":"We develop a scalable, computationally efficient method for the task of\nenergy disaggregation for home appliance monitoring. In this problem the goal\nis to estimate the energy consumption of each appliance over time based on the\ntotal energy-consumption signal of a household. The current state of the art is\nto model the problem as inference in factorial HMMs, and use quadratic\nprogramming to find an approximate solution to the resulting quadratic integer\nprogram. Here we take a more principled approach, better suited to integer\nprogramming problems, and find an approximate optimum by combining convex\nsemidefinite relaxations randomized rounding, as well as a scalable ADMM method\nthat exploits the special structure of the resulting semidefinite program.\nSimulation results both in synthetic and real-world datasets demonstrate the\nsuperiority of our method.","url_abs":"http://arxiv.org/abs/1610.09491v1","url_pdf":"http://arxiv.org/pdf/1610.09491v1.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":"sdp-relaxation-with-randomized-rounding-for","repo_url":"https://github.com/kiarashshaloudegi/FHMM_inference","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"sdp-relaxation-with-randomized-rounding-for","repo_url":"https://github.com/DatenBiene/SDP_relax_for_Energy_Disaggregation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"admm","method_name":"ADMM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}