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The Moment-SOS hierarchy is a powerful methodology that is used to
solve the Generalized Moment Problem (GMP) where the list of
applications in various areas of Science and Engineering is almost
endless. Initially designed for solving polynomial optimization
problems (the simplest example of the GMP), it applies to solving
any instance of the GMP whose description only involves
semi-algebraic functions and sets. It consists of solving a
sequence (a hierarchy) of convex relaxations of the initial
problem, and each convex relaxation is a semidefinite program whose
size increases in the hierarchy.The goal of this book is to
describe in a unified and detailed manner how this methodology
applies to solving various problems in different areas ranging from
Optimization, Probability, Statistics, Signal Processing,
Computational Geometry, Control, Optimal Control and Analysis of a
certain class of nonlinear PDEs. For each application, this
unconventional methodology differs from traditional approaches and
provides an unusual viewpoint. Each chapter is devoted to a
particular application, where the methodology is thoroughly
described and illustrated on some appropriate examples.The
exposition is kept at an appropriate level of detail to aid the
different levels of readers not necessarily familiar with these
tools, to better know and understand this methodology.
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