Prof. Marcia Fampa (PESC) together with Prof. Jon Lee (Univ. of Michigan) has another book published by Springer.

The book entitled "Maximum-Entropy Sampling: Algorithms and Application", addresses 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 problem of calculating a sub-vector of pre-specified size from a multivariate Gaussian random vector, so as to maximize Shannon differential entropy. The text presents state-of-the-art algorithms for MESP and addresses its application in the field of environmental monitoring. From the point of view of mathematical optimization methodology, MESP is rather unique (a non-linear 0/1 program with a non-separable objective function) and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several areas in the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, implicit enumeration algorithms for 0/1 optimization, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analysis.

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