Results 221 to 230 of about 63,174 (260)
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Convex Optimization-Based Beamforming
IEEE Signal Processing Magazine, 2010In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that apply to a broad class of receive, transmit, and network beamformer design problems.
Alex B Gershman +2 more
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An online convex optimization-based framework for convex bilevel optimization
Mathematical Programming, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lingqing Shen +2 more
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Convex programming for disjunctive convex optimization
Mathematical Programming, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sebastián Ceria, João Soares
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Convexity in Non-convex Optimizations of Streaming Applications
2012 IEEE 18th International Conference on Parallel and Distributed Systems, 2012Streaming data applications are frequently pipelined and deployed on application-specific systems to meet performance requirements and resource constraints. Typically, there are several design parameters in the algorithms and architectures used that impact the application performance as well as resource utilization. Efficient exploration of this design
Shobana Padmanabhan +2 more
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Optimization Methods and Software, 2010
Convex optimization theory, by Dimitri P. Bertsekas, Athena Scientific, June 2009, 256 pp., $59.00 (hardcover), ISBN: 1-886529-31-0, 978-1-886529-31-1 The textbook, Convex Optimization Theory (Athe...
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Convex optimization theory, by Dimitri P. Bertsekas, Athena Scientific, June 2009, 256 pp., $59.00 (hardcover), ISBN: 1-886529-31-0, 978-1-886529-31-1 The textbook, Convex Optimization Theory (Athe...
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RAIRO - Operations Research, 2019
Many optimization problems are formulated from a real scenario involving incomplete information due to uncertainty in reality. The uncertainties can be expressed with appropriate probability distributions or fuzzy numbers with a membership function, if enough information can be accessed for the construction of either the probability density function or
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Many optimization problems are formulated from a real scenario involving incomplete information due to uncertainty in reality. The uncertainties can be expressed with appropriate probability distributions or fuzzy numbers with a membership function, if enough information can be accessed for the construction of either the probability density function or
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A tutorial on convex optimization
Proceedings of the 2004 American Control Conference, 2004In recent years, convex optimization has become a computational tool of central importance in engineering, thanks to it's ability to solve very large, practical engineering problems reliably and efficiently. The goal of this tutorial is to give an overview of the basic concepts of convex sets, functions and convex optimization problems, so that the ...
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Depth-Optimized Convexity Cuts
Annals of Operations Research, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jonathan Eckstein, Mikhail Nediak
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Convex Optimization With Convex Constraints
2001In this chapter we want to solve the problem minf(x) | x ∈ C, where f is a convex function on ℝ n , and C is a convex, nonempty subset of ℝ n . A point x* ∈ C is a global solution, or more simply a solution to this problem, or a minimizer of f on C, if f(x*) ≤ f(x), ∀x ∈ C. We say that x* is a local solution to this problem if there exists a relatively
Monique Florenzano, Cuong Le Van
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Optimization on directionally convex sets
Central European Journal of Operations Research, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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