PPT-Distributed Stochastic Optimization

via Correlated Scheduling Michael J Neely University of Southern California httpwwwbcfuscedumjneely 1 2 Fusion Center Observation ω 1 t Observation ω 2 t 1 Distributed sensor reports. Duchi Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA 94720 alekhjduchi eecsberkeleyedu Abstract We analyze the convergence of gradientbased optimization algorithms whose updates depend on dela &#x/MCI; 0 ;&#x/MCI; 0 ;STOCHASTIC PTIMIZATION &#x/Att;¬he; [/;ott;&#xom ];&#x/BBo;&#xx [1; 3;.25;“ 1; 5;
.02;t ];&#x/Sub;&#xtype;&#x /Fo;&#xote Regrets and . Kidneys. Intro to Online Stochastic Optimization. Data revealed over time. Distribution . of future events is known. Under time constraints. Limits amount of . sampling/simulation. Solve these problems with two black boxes:. Non-Convex Utilities and Costs. Michael J. Neely. University of Southern California. http://www-rcf.usc.edu/~mjneely. Information Theory and Applications Workshop (ITA), Feb. 2010. *Sponsored in part by the DARPA IT-MANET Program,. Anupam. Gupta. Carnegie Mellon University. stochastic optimization. Question: . How to model uncertainty in the inputs?. data may not yet be available. obtaining exact data is difficult/expensive/time-consuming. Policies. October 25. , 2012. Warren Powell. CASTLE Laboratory. Princeton University. http://www.castlelab.princeton.edu . © . 2012 . Warren B. Powell, Princeton University. © 2012 Warren B. Powell. Approximate Algorithms. Alessandro Farinelli. Approximate Algorithms: outline. No guarantees. DSA-1, MGM-1 (exchange individual assignments). Max-Sum (exchange functions). Off-Line guarantees. K-optimality and extensions. relaxations. via statistical query complexity. Based on:. V. F.. , Will Perkins, Santosh . Vempala. . . On the Complexity of Random Satisfiability Problems with Planted . Solutions.. STOC 2015. V. F.. Zhenhong. Chen, . Yanyan. . Lan. , . Jiafeng. . Guo. , Jun . Xu. , and . Xueqi. Cheng . CAS Key Laboratory of Network Data Science and Technology,. Institute . of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. Anupam Gupta. Carnegie Mellon University. SODA . 2018, New Orleans. stochastic optimization. Question. : . How to . model and solve problems with . uncertainty in . input/actions?. data . not . yet . Diederik. P. . Kingma. . Jimmy Lei Ba. Presented by . Xinxin. . Zuo. 10/20/2017. Outline. What is Adam. The optimization algorithm. . Bias correction. Bounded . update. Relations with Other approaches. relaxations. via statistical query complexity. Based on:. V. F.. , Will Perkins, Santosh . Vempala. . . On the Complexity of Random Satisfiability Problems with Planted . Solutions.. STOC 2015. V. F.. . storage. . with. . stochastic. . consumption. and production. Erwan Pierre – EDF R&D. SESO 2018 International Thematic . Week. - . Smart Energy and Stochastic Optimization . High . penetration. Data Management for Big Data. 2018-2019 (. s. pring semester). Dario Della Monica. These slides are a modified version of the slides provided with the book. Özsu. and . Valduriez. , . Principles of .