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Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications
https://epubs.siam.org/doi/book/10.1137/1.9781611978094?af=R
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications. <br/> Excerpt This accessible book begins with an elementary and selfcontained chapter on optimal transport on finite state spaces that does not require measure theory or functional analysis. It builds up mathematical theory rigorously and from scratch, aided by intuitive arguments, informal discussion, and carefully selected applications.
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications. <br/> Excerpt This accessible book begins with an elementary and selfcontained chapter on optimal transport on finite state spaces that does not require measure theory or functional analysis. It builds up mathematical theory rigorously and from scratch, aided by intuitive arguments, informal discussion, and carefully selected applications. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2024/1.9781611978094/1.9781611978094/20240917/1.9781611978094.cover.jpg" alttext="cover image"/></p>
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications
doi:10.1137/1.9781611978094
Gero Friesecke
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications
20240917T04:55:25Z
10.1137/1.9781611978094
https://epubs.siam.org/doi/book/10.1137/1.9781611978094?af=R
© 2024 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Uncertainty Quantification: Theory, Implementation, and Applications, Second Edition
https://epubs.siam.org/doi/book/10.1137/1.9781611977844?af=R
Uncertainty Quantification: Theory, Implementation, and Applications, Second Edition. <br/> Uncertainty quantification serves a central role for simulationbased analysis of physical, engineering, and biological applications using mechanistic models. From a broad perspective, the field of uncertainty quantification can be described as the synthesis of mathematical, statistical, and computational theory and methods to quantify uncertainties associated with mechanistic models and their parameters, simulation codes, observed data, and predicted responses for applications whose complexity can preclude sole reliance on samplingbased methods. Hence the field is inherently interdisciplinary and can require the synthesis of theory inherent to considered applications.
Uncertainty Quantification: Theory, Implementation, and Applications, Second Edition. <br/> Uncertainty quantification serves a central role for simulationbased analysis of physical, engineering, and biological applications using mechanistic models. From a broad perspective, the field of uncertainty quantification can be described as the synthesis of mathematical, statistical, and computational theory and methods to quantify uncertainties associated with mechanistic models and their parameters, simulation codes, observed data, and predicted responses for applications whose complexity can preclude sole reliance on samplingbased methods. Hence the field is inherently interdisciplinary and can require the synthesis of theory inherent to considered applications. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/cs/2024/1.9781611977844/1.9781611977844/20240515/1.9781611977844.cover.jpg" alttext="cover image"/></p>
Uncertainty Quantification: Theory, Implementation, and Applications, Second Edition
doi:10.1137/1.9781611977844
Ralph C. Smith
Uncertainty Quantification: Theory, Implementation, and Applications, Second Edition
20240515T06:38:59Z
10.1137/1.9781611977844
https://epubs.siam.org/doi/book/10.1137/1.9781611977844?af=R
© 2024 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Algorithmic Mathematics in Machine Learning
https://epubs.siam.org/doi/book/10.1137/1.9781611977882?af=R
Algorithmic Mathematics in Machine Learning. <br/> The story of machine learning is one of rigorous success. It is frequently employed by scientists and practitioners around the globe in various areas of application ranging from economics to chemistry, from medicine to engineering, from gaming to astronomy, and from speech processing to computer vision. While the remarkable success of machine learning methods speaks for itself, they are often applied in an ad hoc manner without much care for their mathematical foundation or for their algorithmic intricacies. Therefore, we decided to write this book. Our goal is to provide the necessary background on commonly used machine learning algorithms and to highlight important implementational and numerical details. The book is based on a wellreceived practical lab course, which we established within the mathematics studies course at the University of Bonn, Germany, in 2017. The course has been taught and successively enhanced each year since then.
Algorithmic Mathematics in Machine Learning. <br/> The story of machine learning is one of rigorous success. It is frequently employed by scientists and practitioners around the globe in various areas of application ranging from economics to chemistry, from medicine to engineering, from gaming to astronomy, and from speech processing to computer vision. While the remarkable success of machine learning methods speaks for itself, they are often applied in an ad hoc manner without much care for their mathematical foundation or for their algorithmic intricacies. Therefore, we decided to write this book. Our goal is to provide the necessary background on commonly used machine learning algorithms and to highlight important implementational and numerical details. The book is based on a wellreceived practical lab course, which we established within the mathematics studies course at the University of Bonn, Germany, in 2017. The course has been taught and successively enhanced each year since then. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/di/2024/1.9781611977882/1.9781611977882/20240325/1.9781611977882.cover.jpg" alttext="cover image"/></p>
Algorithmic Mathematics in Machine Learning
doi:10.1137/1.9781611977882
Bastian Bohn
Jochen Garcke
Michael Griebel
Algorithmic Mathematics in Machine Learning
20240325T09:13:41Z
10.1137/1.9781611977882
https://epubs.siam.org/doi/book/10.1137/1.9781611977882?af=R
© 2024 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

An Introduction to Convexity, Optimization, and Algorithms
https://epubs.siam.org/doi/book/10.1137/1.9781611977806?af=R
An Introduction to Convexity, Optimization, and Algorithms. <br/> Convex analysis, convex optimization, and algorithms are important topics in modern applied mathematics. In this text, we provide an introduction to a selection of these topics accessible at the advanced undergraduate or beginning graduate level. The only background required is some core knowledge of calculus, linear algebra, and analysis.
An Introduction to Convexity, Optimization, and Algorithms. <br/> Convex analysis, convex optimization, and algorithms are important topics in modern applied mathematics. In this text, we provide an introduction to a selection of these topics accessible at the advanced undergraduate or beginning graduate level. The only background required is some core knowledge of calculus, linear algebra, and analysis. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mo/2023/1.9781611977806/1.9781611977806/20231220/1.9781611977806.cover.jpg" alttext="cover image"/></p>
An Introduction to Convexity, Optimization, and Algorithms
doi:10.1137/1.9781611977806
Heinz H. Bauschke
Walaa M. Moursi
An Introduction to Convexity, Optimization, and Algorithms
20231220T08:46:48Z
10.1137/1.9781611977806
https://epubs.siam.org/doi/book/10.1137/1.9781611977806?af=R
© 2023 by the Society for Industrial and Applied Mathematics

Álgebra Lineal en Ciencia de Datos
https://epubs.siam.org/doi/book/10.1137/1.9781611977387?af=R
Álgebra Lineal en Ciencia de Datos. <br/>
Álgebra Lineal en Ciencia de Datos. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/wc/2022/1.9781611977387/1.9781611977387/20231218/1.9781611977387.cover.jpg" alttext="cover image"/></p>
Álgebra Lineal en Ciencia de Datos
doi:10.1137/1.9781611977387
Gilbert Strang
Álgebra Lineal en Ciencia de Datos
20231218T12:06:04Z
10.1137/1.9781611977387
https://epubs.siam.org/doi/book/10.1137/1.9781611977387?af=R
© 2022 by the WellesleyCambridge Press

Linear Algebra and Learning from Data
https://epubs.siam.org/doi/book/10.1137/1.9780692196380?af=R
Linear Algebra and Learning from Data. <br/>
Linear Algebra and Learning from Data. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/wc/2019/1.9780692196380/1.9780692196380/20230629/1.9780692196380.cover.jpg" alttext="cover image"/></p>
Linear Algebra and Learning from Data
doi:10.1137/1.9780692196380
Gilbert Strang
Linear Algebra and Learning from Data
20230629T02:51:37Z
10.1137/1.9780692196380
https://epubs.siam.org/doi/book/10.1137/1.9780692196380?af=R
© 2019 by the WellesleyCambridge Press

The Mathematics of Data
https://epubs.siam.org/doi/book/10.1137/1.9781470435752?af=R
The Mathematics of Data. <br/>
The Mathematics of Data. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/as/2018/1.9781470435752/1.9781470435752/20230629/1.9781470435752.cover.jpg" alttext="cover image"/></p>
The Mathematics of Data
doi:10.1137/1.9781470435752
The Mathematics of Data
20230629T02:46:57Z
10.1137/1.9781470435752
https://epubs.siam.org/doi/book/10.1137/1.9781470435752?af=R
© 2018 by IAS/Park City Mathematics Series/AMSAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Foundations of Applied Mathematics Volume 2: Algorithms, Approximation, Optimization
https://epubs.siam.org/doi/book/10.1137/1.9781611976069?af=R
Foundations of Applied Mathematics Volume 2: Algorithms, Approximation, Optimization. <br/>
Foundations of Applied Mathematics Volume 2: Algorithms, Approximation, Optimization. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2020/1.9781611976069/1.9781611976069/20230629/1.9781611976069.cover.jpg" alttext="cover image"/></p>
Foundations of Applied Mathematics Volume 2: Algorithms, Approximation, Optimization
doi:10.1137/1.9781611976069
Jeffrey Humpherys
Tyler J. Jarvis
Foundations of Applied Mathematics Volume 2: Algorithms, Approximation, Optimization
20230629T08:26:40Z
10.1137/1.9781611976069
https://epubs.siam.org/doi/book/10.1137/1.9781611976069?af=R
© 2020 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Mathematics of Data Science: A Computational Approach to Clustering and Classification
https://epubs.siam.org/doi/book/10.1137/1.9781611976373?af=R
Mathematics of Data Science: A Computational Approach to Clustering and Classification. <br/>
Mathematics of Data Science: A Computational Approach to Clustering and Classification. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/di/2020/1.9781611976373/1.9781611976373/20230629/1.9781611976373.cover.jpg" alttext="cover image"/></p>
Mathematics of Data Science: A Computational Approach to Clustering and Classification
doi:10.1137/1.9781611976373
Daniela Calvetti
Erkki Somersalo
Mathematics of Data Science: A Computational Approach to Clustering and Classification
20230629T08:26:00Z
10.1137/1.9781611976373
https://epubs.siam.org/doi/book/10.1137/1.9781611976373?af=R
© 2020 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Numerical Linear Algebra with Julia
https://epubs.siam.org/doi/book/10.1137/1.9781611976557?af=R
Numerical Linear Algebra with Julia. <br/>
Numerical Linear Algebra with Julia. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2021/1.9781611976557/1.9781611976557/20230629/1.9781611976557.cover.jpg" alttext="cover image"/></p>
Numerical Linear Algebra with Julia
doi:10.1137/1.9781611976557
Eric Darve
Mary Wootters
Numerical Linear Algebra with Julia
20230629T08:27:20Z
10.1137/1.9781611976557
https://epubs.siam.org/doi/book/10.1137/1.9781611976557?af=R
© 2021 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Numerical Linear Algebra, Twentyfifth Anniversary Edition
https://epubs.siam.org/doi/book/10.1137/1.9781611977165?af=R
Numerical Linear Algebra, Twentyfifth Anniversary Edition. <br/> Since the early 1980. the first author has taught a graduate course in numerical linear algebra at MIT and Cornell. The alumni of this course, now numbering in the hundreds, have been graduate students in all fields of engineering and the physical sciences. This book is an attempt to put this course on paper.
Numerical Linear Algebra, Twentyfifth Anniversary Edition. <br/> Since the early 1980. the first author has taught a graduate course in numerical linear algebra at MIT and Cornell. The alumni of this course, now numbering in the hundreds, have been graduate students in all fields of engineering and the physical sciences. This book is an attempt to put this course on paper. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2022/1.9781611977165/1.9781611977165/20230629/1.9781611977165.cover.jpg" alttext="cover image"/></p>
Numerical Linear Algebra, Twentyfifth Anniversary Edition
doi:10.1137/1.9781611977165
Lloyd N. Trefethen
David Bau
Numerical Linear Algebra, Twentyfifth Anniversary Edition
20230629T08:30:00Z
10.1137/1.9781611977165
https://epubs.siam.org/doi/book/10.1137/1.9781611977165?af=R
© 2022 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

The Less Is More Linear Algebra of Vector Spaces and Matrices
https://epubs.siam.org/doi/book/10.1137/1.9781611977400?af=R
The Less Is More Linear Algebra of Vector Spaces and Matrices. <br/> Linear algebra is one of the pillars of mathematics and a prototype of it in several ways. It trains the mind to organize ideas and results so as to make the most efficient use of them. Linear algebra is deeply theoretical at its core, yet it plays a crucial role in many extremely practical applications. The power of linear algebra is that it is developed for objects satisfying certain conditions that have nothing to do with computing and numbers, and at the same time it is crucial when it comes to simplifying and streamlining numerical calculations.
The Less Is More Linear Algebra of Vector Spaces and Matrices. <br/> Linear algebra is one of the pillars of mathematics and a prototype of it in several ways. It trains the mind to organize ideas and results so as to make the most efficient use of them. Linear algebra is deeply theoretical at its core, yet it plays a crucial role in many extremely practical applications. The power of linear algebra is that it is developed for objects satisfying certain conditions that have nothing to do with computing and numbers, and at the same time it is crucial when it comes to simplifying and streamlining numerical calculations. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2022/1.9781611977400/1.9781611977400/20230629/1.9781611977400.cover.jpg" alttext="cover image"/></p>
The Less Is More Linear Algebra of Vector Spaces and Matrices
doi:10.1137/1.9781611977400
Daniela Calvetti
Erkki Somersalo
The Less Is More Linear Algebra of Vector Spaces and Matrices
20230629T08:32:00Z
10.1137/1.9781611977400
https://epubs.siam.org/doi/book/10.1137/1.9781611977400?af=R
© 2022 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, Second Edition
https://epubs.siam.org/doi/book/10.1137/1.9781611977622?af=R
Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, Second Edition. <br/> Preface to the Second Edition: The second edition features two significant enhancements to the first edition. 1. Python codes were added on top of the existing MATLAB codes to illustrate and demonstrate different aspects of the algorithmic and applicative nature of nonlinear optimization. Since the first edition's publication, Python has become one of the leading software languages for scientific computing and is used in many applications, most notably those arising in data science. Readers interested in implementation may choose to follow either the MATLAB or Python codes which appear, sometimes literally, side by side. A new section on the Python module CVXPY (Section 8.5) describes how to solve convex optimization problems using Python.
Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, Second Edition. <br/> Preface to the Second Edition: The second edition features two significant enhancements to the first edition. 1. Python codes were added on top of the existing MATLAB codes to illustrate and demonstrate different aspects of the algorithmic and applicative nature of nonlinear optimization. Since the first edition's publication, Python has become one of the leading software languages for scientific computing and is used in many applications, most notably those arising in data science. Readers interested in implementation may choose to follow either the MATLAB or Python codes which appear, sometimes literally, side by side. A new section on the Python module CVXPY (Section 8.5) describes how to solve convex optimization problems using Python. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mo/2023/1.9781611977622/1.9781611977622/20230629/1.9781611977622.cover.jpg" alttext="cover image"/></p>
Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, Second Edition
doi:10.1137/1.9781611977622
Amir Beck
Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, Second Edition
20230629T08:36:40Z
10.1137/1.9781611977622
https://epubs.siam.org/doi/book/10.1137/1.9781611977622?af=R
© 2023 by the Society for Industrial and Applied Mathematics

Linear Algebra for Everyone
https://epubs.siam.org/doi/book/10.1137/1.9781733146630?af=R
Linear Algebra for Everyone. <br/>
Linear Algebra for Everyone. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/wc/2020/1.9781733146630/1.9781733146630/20230629/1.9781733146630.cover.jpg" alttext="cover image"/></p>
Linear Algebra for Everyone
doi:10.1137/1.9781733146630
Gilbert Strang
Linear Algebra for Everyone
20230629T02:51:58Z
10.1137/1.9781733146630
https://epubs.siam.org/doi/book/10.1137/1.9781733146630?af=R
© 2020 by the WellesleyCambridge Press

Lecture Notes for Linear Algebra
https://epubs.siam.org/doi/book/10.1137/1.9781733146647?af=R
Lecture Notes for Linear Algebra. <br/>
Lecture Notes for Linear Algebra. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/wc/2021/1.9781733146647/1.9781733146647/20230629/1.9781733146647.cover.jpg" alttext="cover image"/></p>
Lecture Notes for Linear Algebra
doi:10.1137/1.9781733146647
Gilbert Strang
Lecture Notes for Linear Algebra
20230629T02:52:17Z
10.1137/1.9781733146647
https://epubs.siam.org/doi/book/10.1137/1.9781733146647?af=R
© 2021 by the WellesleyCambridge Press

Introduction to Numerical Linear Algebra
https://epubs.siam.org/doi/book/10.1137/1.9781611976922?af=R
Introduction to Numerical Linear Algebra. <br/> G. H. Hardy published A Mathematician's Apology in 1940. The sense of the term is that of apologia, a defense of a field. It could be said (and some of my friends have said) that a more accurate title for the present piece would have been Confessions of a Numerical Analyst. To be sure, this essay differs in many ways from Hardy's, containing more biographical material and also more mathematics, especially in the second half. But its purpose is the same, a serious and personal meditation about mathematics
Introduction to Numerical Linear Algebra. <br/> G. H. Hardy published A Mathematician's Apology in 1940. The sense of the term is that of apologia, a defense of a field. It could be said (and some of my friends have said) that a more accurate title for the present piece would have been Confessions of a Numerical Analyst. To be sure, this essay differs in many ways from Hardy's, containing more biographical material and also more mathematics, especially in the second half. But its purpose is the same, a serious and personal meditation about mathematics <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2022/1.9781611976922/1.9781611976922/20230111/1.9781611976922.cover.jpg" alttext="cover image"/></p>
Introduction to Numerical Linear Algebra
doi:10.1137/1.9781611976922
Christoph Börgers
Introduction to Numerical Linear Algebra
20230111T02:32:52Z
10.1137/1.9781611976922
https://epubs.siam.org/doi/book/10.1137/1.9781611976922?af=R
© 2022 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation and Perspectives
https://epubs.siam.org/doi/book/10.1137/1.9781611976991?af=R
Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation and Perspectives. <br/> Do you know the difference between an optimist and a pessimist? The former believes we live in the best possible world, and the latter is afraid that the former might be right. … In that sense, this is an optimistic book in that it shows that apparently daunting tasks (such as minimizing nonlinear nonconvex functions subject to nonlinear equality and inequality constraints) may not be completely intractable, even in the worstpossible case. It is also a pessimistic book because the complexity results obtained are often relatively poor from the practical point of view while being theoretically sharp.
Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation and Perspectives. <br/> Do you know the difference between an optimist and a pessimist? The former believes we live in the best possible world, and the latter is afraid that the former might be right. … In that sense, this is an optimistic book in that it shows that apparently daunting tasks (such as minimizing nonlinear nonconvex functions subject to nonlinear equality and inequality constraints) may not be completely intractable, even in the worstpossible case. It is also a pessimistic book because the complexity results obtained are often relatively poor from the practical point of view while being theoretically sharp. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mo/2022/1.9781611976991/1.9781611976991/20221104/1.9781611976991.cover.jpg" alttext="cover image"/></p>
Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation and Perspectives
doi:10.1137/1.9781611976991
Coralia Cartis
Nicholas I. M. Gould
Philippe L. Toint
Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation and Perspectives
20221104T08:28:03Z
10.1137/1.9781611976991
https://epubs.siam.org/doi/book/10.1137/1.9781611976991?af=R
© 2022 by the Society for Industrial and Applied Mathematics

Foundations of Computational Imaging: A ModelBased Approach
https://epubs.siam.org/doi/book/10.1137/1.9781611977134?af=R
Foundations of Computational Imaging: A ModelBased Approach. <br/> When I first started writing this book 20 years ago, Computational Imaging did not yet exist as a field. But at the time, I had a growing sense that the next generation of imaging systems required the integration of algorithms with sensors and that a suite of common analytical and computational tools was emerging for solving these problems and designing these systems.
Foundations of Computational Imaging: A ModelBased Approach. <br/> When I first started writing this book 20 years ago, Computational Imaging did not yet exist as a field. But at the time, I had a growing sense that the next generation of imaging systems required the integration of algorithms with sensors and that a suite of common analytical and computational tools was emerging for solving these problems and designing these systems. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2022/1.9781611977134/1.9781611977134/20221104/1.9781611977134.cover.jpg" alttext="cover image"/></p>
Foundations of Computational Imaging: A ModelBased Approach
doi:10.1137/1.9781611977134
Charles A. Bouman
Foundations of Computational Imaging: A ModelBased Approach
20221104T08:16:43Z
10.1137/1.9781611977134
https://epubs.siam.org/doi/book/10.1137/1.9781611977134?af=R
© 2022 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

A Toolbox for Digital Twins: From ModelBased to DataDriven
https://epubs.siam.org/doi/book/10.1137/1.9781611976977?af=R
A Toolbox for Digital Twins: From ModelBased to DataDriven. <br/> A digital twin (DT) is a numerical model (in silico) of a system that runs alongside its physical counterpart and exchanges (bidirectional) information with it. The digital twin, once suitably “tuned” to the physical plant, can then be used for better understanding and analysis of the physical system; experimenting on new regimes or treatments; predicting the performance; quantifying uncertainty, measuring risk, and making decisions.
A Toolbox for Digital Twins: From ModelBased to DataDriven. <br/> A digital twin (DT) is a numerical model (in silico) of a system that runs alongside its physical counterpart and exchanges (bidirectional) information with it. The digital twin, once suitably “tuned” to the physical plant, can then be used for better understanding and analysis of the physical system; experimenting on new regimes or treatments; predicting the performance; quantifying uncertainty, measuring risk, and making decisions. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mn/2022/1.9781611976977/1.9781611976977/20220804/1.9781611976977.cover.jpg" alttext="cover image"/></p>
A Toolbox for Digital Twins: From ModelBased to DataDriven
doi:10.1137/1.9781611976977
Mark Asch
A Toolbox for Digital Twins: From ModelBased to DataDriven
20220804T08:34:09Z
10.1137/1.9781611976977
https://epubs.siam.org/doi/book/10.1137/1.9781611976977?af=R
© 2022 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.

Sparse Polynomial Approximation of HighDimensional Functions
https://epubs.siam.org/doi/book/10.1137/1.9781611976885?af=R
Sparse Polynomial Approximation of HighDimensional Functions. <br/> Over seventy years ago, Richard Bellman coined the term the curse of dimensionality to describe phenomena and computational challenges that arise in high dimensions. These challenges, in tandem with the ubiquity of highdimensional functions in realworld applications, have led to a lengthy, focused research effort on highdimensional approximation—that is, the development of methods for approximating functions of many variables accurately and efficiently from data. This book is about one of the latest chapters in this long and ongoing story: sparse polynomial approximation methods. Such methods differ from more classical techniques in highdimensional approximation in that they combine unstructured grids, typically obtained via Monte Carlo sampling, with ideas from best sterm approximation, least squares, sparse recovery, and compressed sensing. This allows one to address high or even formally infinitedimensional problems in which the target function is smooth, with rates of convergence that can, in certain settings, be independent of the dimension. For suitable classes of problems, such methods provably mitigate the curse of dimensionality to a substantial extent. It is due in part to these desirable theoretical properties that sparse polynomial approximation methods have emerged over the past 10–15 years as useful tools for various highdimensional approximation tasks arising in a range of applications in computational science and engineering. This includes problems involving parametric models and, in particular, parametric differential equations.
Sparse Polynomial Approximation of HighDimensional Functions. <br/> Over seventy years ago, Richard Bellman coined the term the curse of dimensionality to describe phenomena and computational challenges that arise in high dimensions. These challenges, in tandem with the ubiquity of highdimensional functions in realworld applications, have led to a lengthy, focused research effort on highdimensional approximation—that is, the development of methods for approximating functions of many variables accurately and efficiently from data. This book is about one of the latest chapters in this long and ongoing story: sparse polynomial approximation methods. Such methods differ from more classical techniques in highdimensional approximation in that they combine unstructured grids, typically obtained via Monte Carlo sampling, with ideas from best sterm approximation, least squares, sparse recovery, and compressed sensing. This allows one to address high or even formally infinitedimensional problems in which the target function is smooth, with rates of convergence that can, in certain settings, be independent of the dimension. For suitable classes of problems, such methods provably mitigate the curse of dimensionality to a substantial extent. It is due in part to these desirable theoretical properties that sparse polynomial approximation methods have emerged over the past 10–15 years as useful tools for various highdimensional approximation tasks arising in a range of applications in computational science and engineering. This includes problems involving parametric models and, in particular, parametric differential equations. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/cs/2022/1.9781611976885/1.9781611976885/20220216/1.9781611976885.cover.jpg" alttext="cover image"/></p>
Sparse Polynomial Approximation of HighDimensional Functions
doi:10.1137/1.9781611976885
Ben Adcock
Simone Brugiapaglia
Clayton G. Webster
Sparse Polynomial Approximation of HighDimensional Functions
20220216T09:45:47Z
10.1137/1.9781611976885
https://epubs.siam.org/doi/book/10.1137/1.9781611976885?af=R
© 2022 by the Society for Industrial and Applied MathematicsAll rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the written permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA 191042688 USA.