000			nam a22 4500
999			_c33082 _d33082
008			240320b xxu||||| |||| 00| 0 eng d
020			_a9783031130779
082			_a519.3 _bFAM
100			_aFampa, Marcia
245			_aMaximum entropy sampling : algorithms and application
260			_bSpringer, _c2022 _aCham :
300			_axvii, 195 p.; _bill., _c24 cm
365			_d93.50 _b109.99 _c€
490			_aSpringer series in operations research and financial engineering
504			_aIncludes bibliographical references and index.
520			_aThis 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.
650			_aMathematical optimization
650			_aMaximum entropy method
700			_aLee, Jon
942			_2ddc _cBK