Global and Local Optimization Algorithms for Optimal Signal Set Design

The problem of choosing an optimal signal set for non-Gaussian detection was reduced to a smooth inequality constrained mini-max nonlinear programming problem by Gockenbach and Kearsley. Here we consider the application of several optimization algorithms, both global and local, to this problem. The...

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Detalles Bibliográficos
Autor principal: Kearsley, Anthony J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: [Gaithersburg, MD] : U.S. Dept. of Commerce, National Institute of Standards and Technology 2001
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4862810/
https://www.ncbi.nlm.nih.gov/pubmed/27500032
http://dx.doi.org/10.6028/jres.106.019
Descripción
Sumario:The problem of choosing an optimal signal set for non-Gaussian detection was reduced to a smooth inequality constrained mini-max nonlinear programming problem by Gockenbach and Kearsley. Here we consider the application of several optimization algorithms, both global and local, to this problem. The most promising results are obtained when special-purpose sequential quadratic programming (SQP) algorithms are embedded into stochastic global algorithms.

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