{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T22:06:20Z","timestamp":1771625180851,"version":"3.50.1"},"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":[[2019,8]]},"abstract":"<jats:p>Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning problems such as black-box learning and bandit feedback,\nADMM could fail because the explicit gradients of these problems are difficult or even infeasible to obtain. Zeroth-order (gradient-free) methods can effectively solve these problems due to that the objective function values\nare only required in the optimization. Recently, though there exist a few zeroth-order ADMM methods, they build on the convexity of objective function. Clearly, these existing zeroth-order methods are limited in many applications.\nIn the paper, thus, we propose a class of fast zeroth-order stochastic ADMM methods (\\emph{i.e.}, ZO-SVRG-ADMM and ZO-SAGA-ADMM) for solving nonconvex problems with multiple nonsmooth penalties, based on the\ncoordinate smoothing gradient estimator. Moreover, we prove that both the ZO-SVRG-ADMM and ZO-SAGA-ADMM have convergence rate of $O(1\/T)$, where $T$ denotes the number of iterations. In particular, our methods not only reach the best convergence rate of $O(1\/T)$ for the nonconvex optimization, but also are able to effectively solve many complex machine learning problems with multiple regularized penalties and constraints.\nFinally, we conduct the experiments of black-box binary classification and structured adversarial attack on black-box deep neural network to validate the efficiency of our algorithms.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/354","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"2549-2555","source":"Crossref","is-referenced-by-count":20,"title":["Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization"],"prefix":"10.24963","author":[{"given":"Feihu","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, USA"}]},{"given":"Shangqian","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, USA"}]},{"given":"Songcan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics"},{"name":"MIIT Key Laboratory of Pattern Analysis & Machine Intelligence, China"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, USA"},{"name":"JD Finance America Corporation"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:48:45Z","timestamp":1564300125000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/354"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/354","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}