Téléchargez le livre : Maximum-Entropy Sampling

Maximum-Entropy Sampling

Algorithms and Application de

,

Éditeur :

Springer


Collection :

Springer Series in Operations Research and Financial Engineering

Paru le : 2022-10-29

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Description
This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificed size from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics.

 
Pages
195 pages
Collection
Springer Series in Operations Research and Financial Engineering
Parution
2022-10-29
Marque
Springer
EAN papier
9783031130779
EAN PDF
9783031130786
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
6339 Ko
Prix
126,59 €

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