The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Real-Time Convex Optimization in Signal Processing
Abstract
Convex optimization has been used in signal processing for a long time to choose coefficients for use in fast (linear) algorithms, such as in filter or array design; more recently, it has been used to carry out (nonlinear) processing on the signal itself. Examples of the latter case include total variation denoising, compressed sensing, fault detection, and image classification. In both scenarios, the optimization is carried out on time scales of seconds or minutes and without strict time constraints. Convex optimization has traditionally been considered computationally expensive, so its use has been limited to applications where plenty of time is available. Such restrictions are no longer justified. The combination of dramatically increased computing power, modern algorithms, and new coding approaches has delivered an enormous speed increase, which makes it possible to solve modest-sized convex optimization problems on microsecond or millisecond time scales and with strict deadlines. This enables real-time convex optimization in signal processing.
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Cites
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Acikmese, B., Carson, J. M., & Blackmore, L. (2013) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2012.2237346 · Cited by 316
An optimization-based approach to human body motion capture using inertial sensors
Kok, M., Hol, J. D., & Schön, T. B. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140824-6-za-1003.02252 · Cited by 131
Large-Scale Convex Optimization for Dense Wireless Cooperative Networks
Shi, Y., Zhang, J., O'Donoghue, B., & Letaief, K. B. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2443731 · Cited by 118
OpEn: Code Generation for Embedded Nonconvex Optimization
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Metadata
- Authors
- Mattingley, J., & Boyd, S.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2010-05
- Volume
- 27
- Issue
- 3
- Pages
- 50-61
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2101986122
- Cited by
- 300
- References
- 69
- FWCI
- 25.58
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
