2011 ConvexOptimizationfromEmbeddedR

• (Boyd, 2011) ⇒ Stephen Boyd. (2011). “Convex Optimization: From Embedded Real-time to Large-scale Distributed.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011). doi:10.1145/2020408.2020410

Subject Headings: Convex Optimization.

Cited By

• http://scholar.google.com/scholar?q=%22Convex+optimization%3A+from+embedded+real-time+to+large-scale+distributed%22+2011
• http://portal.acm.org/citation.cfm?doid=2020408.2020410&preflayout=flat#citedby

Quotes

Abstract

Convex optimization has emerged as useful tool for applications that include data analysis and model fitting, resource allocation, engineering design, network design and optimization, finance, and control and signal processing. After an overview, the talk will focus on two extremes: real-time embedded convex optimization, and distributed convex optimization. Code generation can be used to generate extremely efficient and reliable solvers for small problems, that can execute in milliseconds or microseconds, and are ideal for embedding in real-time systems. At the other extreme, we describe methods for large-scale distributed optimization, which coordinate many solvers to solve enormous problems.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 ConvexOptimizationfromEmbeddedR	Stephen Boyd			Convex Optimization: From Embedded Real-time to Large-scale Distributed				10.1145/2020408.2020410