Results 271 to 280 of about 2,017,112 (308)

A tutorial on convex optimization

Proceedings of the 2004 American Control Conference, 2004
In 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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Convex Optimization-Based Beamforming

IEEE Signal Processing Magazine, 2010
In 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, Nicholas D. Sidiropoulos, Shahram Shahbazpanahi, Mats Bengtsson, Björn E. Ottersten   +4 more
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Convex Grey Optimization

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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Convex Optimization With Convex Constraints

2001
In 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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Depth-Optimized Convexity Cuts

Annals of Operations Research, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jonathan Eckstein, Mikhail Nediak
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Optimization on directionally convex sets

Central European Journal of Operations Research, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Nesterov's Method for Convex Optimization

SIAM Review, 2023
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On A characterization of optimality in convex programming

Mathematical Programming, 1976
Necessary and sufficient conditions for optimality are given, for convex programming problems, without constraint qualification, in terms of a single mathematical program, which can be chosen to be bilinear.
Adi Ben-Israel, Aharon Ben-Tal
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Duality in Reverse Convex Optimization

SIAM Journal on Optimization, 1998
Summary: A duality theorem for the general problem of minimizing an extended real-valued convex function on a locally convex linear space under a reverse convex constraint is considered. In the particular case of the distance to a reverse convex subset in a normed linear space, we recover as a corollary a duality theorem due to \textit{C.
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Partly Convex and Convex-Monotonic Optimization Problems

2005
A class of nonconvex optimization problems is studied that exhibits partial convexity combined with partial monotonicity. To exploit this particular hybrid structure a natural approach is to use a branch and bound scheme with branching performed on the nonconvex variables and bounds computed by lagrangian or convex relaxation.
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