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Mathematics of Operations Research, 1998
We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization.
Ben-Tal, A., Nemirovski, A.
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We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization.
Ben-Tal, A., Nemirovski, A.
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011
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 ...
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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 ...
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Depth-Optimized Convexity Cuts
Annals of Operations Research, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Eckstein, Jonathan, Nediak, Mikhail
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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.
A.B. Gershman +4 more
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Solving Nonconvex Optimal Control Problems by Convex Optimization
Journal of Guidance, Control, and Dynamics, 2013Motivated by aerospace applications, this paper presents a methodology to use second-order cone programming to solve nonconvex optimal control problems. The nonconvexity arises from the presence of concave state inequality constraints and nonlinear terminal equality constraints.
Xinfu Liu, Ping Lu
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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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Convex Optimization Via Feedbacks
SIAM Journal on Control and Optimization, 1998Summary: Three dynamical systems are associated with a problem of convex optimization in a finite-dimensional space. For system trajectories \( x(t) \), the ratios \( x(t)/t \) are, respectively, (i) solution tracking (staying within the solution set \( X^0 \)), (ii) solution abandoning (reaching \( X^0 \) as time \( t \) goes back to the initial ...
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