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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
