The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics
Open accessAbstract
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.
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Citations
Cites
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Beck, A., & Teboulle, M. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080716542 · Cited by 11,952
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization
Shi, W., Ling, Q., Wu, G., & Yin, W. (2015) · SIAM Journal on Optimization · doi:10.1137/14096668x · Cited by 1,261 · cited 1×
“Surprisingly, we can also decentralize the communication requirements of gradient methods for decomposable objectives with only minor modifications [31].”
Cited by
Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs
Nedić, A., Olshevsky, A., & Shi, W. (2017) · SIAM Journal on Optimization · doi:10.1137/16m1084316 · Cited by 1,069
“The distributed algorithms designed over such special structure are usually fast in practice and mostly used in machine learning to handle big-data in a cluster of computers [6,8].”
BackgroundMachine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks
Sharma, S. K., & Wang, X. (2017) · IEEE Access · doi:10.1109/access.2017.2682640 · Cited by 241
“The widely used linear algebra methods such as Cholesky decomposition and matrix multiplications, and convex optimization algorithms can not be straightforwardly applied for big data analytics because of extremely large sizes of data as well as the involved parameters [20].”
MethodData-driven event triggering for IoT applications
Kolios, P., Panayiotou, C., Ellinas, G., & Polycarpou, M. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2564428 · Cited by 60
On the Linear Convergence of Distributed Optimization over Directed Graphs
Xi, C., & Khan, U. A. (2015) · arXiv · doi:10.48550/arxiv.1510.02149 · Cited by 29
Metadata
- Authors
- Cevher, V., Becker, S., & Schmidt, M.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2014-09
- Volume
- 31
- Issue
- 5
- Pages
- 32-43
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W3123607434
- Cited by
- 300
- References
- 2
- FWCI
- 41.92
- Type
- journal article
- Language
- en
- Open access
- green
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- Cevher, V., Becker, S., & Schmidt, M. (2014). Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2014.2329397
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