{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:33:39Z","timestamp":1723016019335},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>We develop a general accelerated proximal coordinate descent algorithm in distributed settings (Dis- APCG) for the optimization problem that minimizes the sum of two convex functions: the first part f is smooth with a gradient oracle, and the other one \u03a8 is separable with respect to blocks of coordinate and has a simple known structure (e.g., L1 norm). Our algorithm gets new accelerated convergence rate in the case that f is strongly con- vex by making use of modern parallel structures, and includes previous non-strongly case as a special case. We further present efficient implementations to avoid full-dimensional operations in each step, significantly reducing the computation cost. Experiments on the regularized empirical risk minimization problem demonstrate the effectiveness of our algorithm and match our theoretical findings.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/370","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"2655-2661","source":"Crossref","is-referenced-by-count":0,"title":["Distributed Accelerated Proximal Coordinate Gradient Methods"],"prefix":"10.24963","author":[{"given":"Yong","family":"Ren","sequence":"first","affiliation":[{"name":"Center for Bio-Inspired Computing Research"},{"name":"State Key Lab for Intell. Tech. & Systems"},{"name":"Dept. of Comp. Sci. & Tech., TNList Lab, Tsinghua University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tsinghua National Laboratory for Information Science and Technology (TNList)"},{"name":"State Key Lab for Intelligent Technology and Systems"},{"name":"Center for Brain-Inspired Computing Research (CBICR)"},{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:53:35Z","timestamp":1501242815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/370"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/370","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}