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Könyv Maximum-Entropy Sampling Marcia Fampa

Maximum-Entropy Sampling

Algorithms and Application

Szerző: Marcia Fampa, Jon Lee
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: Springer, Berlin
Elérhetőség: Beszállítói készleten
Küldés 10-13 napon belül
46 340 Ft
This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), wh...

Információk a könyvről

Szerző
Marcia Fampa, Jon Lee
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2022
oldal
195
EAN
9783031130779
Enbook ID
39439304
Kiadó
Springer, Berlin
Súly
494
Méretek
155 x 235 x 18

Teljes leírás

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. 

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