PDF-An Asynchronous Parallel Stochastic Coordinate Descent Algorithm

Weshowthatlinearconvergencecanbeattainedifan147essentialstrongconvexity148property3holdswhilesublinearconvergenceata1471K148ratecanbeprovedforgeneralconvexfunctionsOuranalysisalsode. Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr Bradley jkbradlecscmuedu Aapo Kyrola akyrolacscmuedu Danny Bickson bicksoncscmuedu Carlos Guestrin guestrincscmuedu Carnegie Mellon University 5000 Forbes Ave Pittsburgh PA 15213 USA Abstract We propose Shotgun a parallel coordi nate descent algorit This can be generalized to any dimension brPage 9br Example of 2D gradient pic of the MATLAB demo Illustration of the gradient in 2D Example of 2D gradient pic of the MATLAB demo Gradient descent works in 2D brPage 10br 10 Generalization to multiple Some of the fastest known algorithms for certain tasks rely on chance. Stochastic/Randomized Algorithms. Two common variations. Monte Carlo. Las Vegas. We have already encountered some of both in this class. Burrows-Wheeler. Compression and Decompression. James A. Edwards. , Uzi Vishkin. University of Maryland. Introduction. Lossless data compression. Common tool . . better use of . memory (e.g., disk space). Gradient Descent Methods. Jakub . Kone. čný. . (joint work with Peter . Richt. árik. ). University of Edinburgh. Introduction. Large scale problem setting. Problems are often structured. Frequently arising in machine learning. Algorithms for Efficient. Large Margin . Structured Prediction. Ming-Wei Chang . and Scott Wen-tau Yih. Microsoft Research. 1. Motivation. . Many NLP tasks are structured. Parsing, Coreference, Chunking, SRL, Summarization, Machine translation, Entity Linking,…. Bassily. Adam Smith . Abhradeep. Thakurta. . . . . Penn State . Yahoo! Labs. . Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. 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. On . the development of numerical . parallel algorithms . for the insetting procedure. Master of Science in . Communication & Information Systems.  . Department of Informatics & Communications. Solving the SVP in the Ideal Lattice of 128 dimensions. Tsukasa Ishiguro (KDDI R&D Laboratories). Shinsaku Kiyomoto (KDDI R&D Laboratories) . Yutaka Miyake (KDDI R&D Laboratories). Tsuyoshi Takagi. 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. Computing. Jie Liu. , . Ph.D.. Professor. Computer Science Division. Western Oregon University. Monmouth, Oregon, USA. liuj@wou.edu. outline. The . fastest computers. The PRAM model. The O(1) algorithm that finds the max. John N TsitsiklisRoom 35-214 Laboratory for Information and Decision Systems MIT CambridgeMA 02139 USAAbstract We consider an iterative process in which one out of a finite setof infinitely many times