Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics
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- 18 August 2014
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Signal Processing Magazine
- Vol. 31 (5), 32-43
- https://doi.org/10.1109/msp.2014.2329397
Abstract
This article reviews recent advances in convex optimization algorithms for big data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques such as first-order methods and randomization for scalability, and survey the important role of parallel and distributed computation. The new big data algorithms are based on surprisingly simple principles and attain staggering accelerations even on classical problems.Keywords
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