PPT-Stochastic Network Optimization

tutorial M J Neely University of Southern California See detailed derivations for these results in M J Neely Stochastic Network Optimization with Application to Communication and Queueing. &#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. via Correlated Scheduling. Michael J. Neely. University of Southern California. http://www-bcf.usc.edu/~mjneely. 1. 2. Fusion . Center. Observation . ω. 1. (t). Observation . ω. 2. (t). 1. Distributed sensor reports. and. Distributed Network Algorithms. Rajmohan Rajaraman. Northeastern University, Boston. May 2012. Chennai Network Optimization Workshop. AND and DNA. 1. Overview of the 4 Sessions. Random walks. Percolation processes. 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.. Monte Carlo Tree Search. Minimax. search fails for games with deep trees, large branching factor, and no simple heuristics. Go: branching factor . 361 (19x19 board). Monte Carlo Tree Search. Instead . 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. Non-convex optimization. All loss-functions that are not convex: not very informative.. Global optimality: too strong. Weaker notions of optimality?. What is a saddle point?. Different kinds of critical/stationary points.