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Moment-sos Hierarchy, The: Lectures In Probability, Statistics, Computational Geometry, Control And Nonlinear Pdes, Didier Henrion, Jean Bernard Lasserre, Milan Korda


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Автор: Didier Henrion, Jean Bernard Lasserre, Milan Korda
Название:  Moment-sos Hierarchy, The: Lectures In Probability, Statistics, Computational Geometry, Control And Nonlinear Pdes
ISBN: 9781786348531
Издательство: World Scientific Publishing
Классификация:
ISBN-10: 1786348535
Обложка/Формат: Hardcover
Страницы: 248
Вес: 0.50 кг.
Дата издания: 17.11.2020
Серия: Series on optimization and its applications
Язык: English
Размер: 22.91 x 15.19 x 1.60 cm
Читательская аудитория: Tertiary education (us: college)
Ключевые слова: Optimization, MATHEMATICS / Differential Equations / Ordinary,MATHEMATICS / Optimization,MATHEMATICS / Probability & Statistics / General
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Поставляется из: Англии
Описание:

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.




An Introduction to Polynomial and Semi-Algebraic Optimization

Автор: Lasserre
Название: An Introduction to Polynomial and Semi-Algebraic Optimization
ISBN: 110763069X ISBN-13(EAN): 9781107630697
Издательство: Cambridge Academ
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Цена: 7286.00 р.
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Описание: This is the first comprehensive introduction to the powerful moment approach for solving global optimization problems. Graduate students, engineers and researchers entering the field can use this book to understand, experiment with and master this new approach through the simple worked examples provided.

An Introduction to Polynomial and Semi-Algebraic Optimization

Автор: Lasserre
Название: An Introduction to Polynomial and Semi-Algebraic Optimization
ISBN: 1107060575 ISBN-13(EAN): 9781107060579
Издательство: Cambridge Academ
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Цена: 21226.00 р.
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Описание: This is the first comprehensive introduction to the powerful moment approach for solving global optimization problems. Graduate students, engineers and researchers entering the field can use this book to understand, experiment with and master this new approach through the simple worked examples provided.

Sparse Solutions of Underdetermined Linear Systems

Автор: Ming-Jun Lai, Yang Wang
Название: Sparse Solutions of Underdetermined Linear Systems
ISBN: 1611976502 ISBN-13(EAN): 9781611976502
Издательство: Mare Nostrum (Eurospan)
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Цена: 11161.00 р.
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Описание: This textbook presents a special solution of underdetermined linear systems where the number of nonzero entries in the solution is very small compared to the total number of entries. This is called sparse solution. As underdetermined linear systems can be very different, the authors explain how to compute a sparse solution by many approaches.Sparse Solutions of Underdetermined Linear Systems:Contains 72 algorithms for finding sparse solutions of underdetermined linear systems and their applications for matrix completion, graph clustering, and phase retrieval.Provides a detailed explanation of these algorithms including derivations and convergence analysis.Includes exercises for each chapter to help the reader understand the material.This textbook is appropriate for graduate students in math and applied math, computer science, statistics, data science, and engineering. Advisors and postdocs will also find the book of interest.It is appropriate for the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory.


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