Society for Industrial and Applied Mathematics: Optimization Theory and Mathematical Programming
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Society for Industrial and Applied Mathematics: Optimization Theory and Mathematical Programming
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DataDriven Methods for Dynamic Systems
https://epubs.siam.org/doi/book/10.1137/1.9781611978162?af=R
DataDriven Methods for Dynamic Systems. <br/> Excerpt This book grew out of multiple stimulating conversations with Nathan Kutz while I was a postdoc at the University of Washington. It began with joking about taking our favorite dynamical systems textbook, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields by J. Guckenheimer and P. Holmes, and rewriting it chapter by chapter with modern datadriven techniques. Our idea was to highlight how so much of the dynamical systems theory we were taught can now be explicitly implemented using the widely available computational techniques that typically fall under the umbrella of data analysis. Ideas as simple as optimally fitting data to models could lead the reader right back to Guckenheimer and Holmes's textbook since one is now in a position to apply all the pencilandpaper techniques that have been developed over more than a century of dynamical systems theory. Similarly, finding changes of variable that recast complex dynamics in the form of the phenomenological models that have been examined in detail by not only Guckenheimer and Holmes but also nearly anyone who has taught or published in dynamical systems is now accessible using neural networks. The goal has always been to showcase a suite of computational methods that can be combined with analysis to better understand the increasingly complicated models and datasets that describe our complex world.
DataDriven Methods for Dynamic Systems. <br/> Excerpt This book grew out of multiple stimulating conversations with Nathan Kutz while I was a postdoc at the University of Washington. It began with joking about taking our favorite dynamical systems textbook, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields by J. Guckenheimer and P. Holmes, and rewriting it chapter by chapter with modern datadriven techniques. Our idea was to highlight how so much of the dynamical systems theory we were taught can now be explicitly implemented using the widely available computational techniques that typically fall under the umbrella of data analysis. Ideas as simple as optimally fitting data to models could lead the reader right back to Guckenheimer and Holmes's textbook since one is now in a position to apply all the pencilandpaper techniques that have been developed over more than a century of dynamical systems theory. Similarly, finding changes of variable that recast complex dynamics in the form of the phenomenological models that have been examined in detail by not only Guckenheimer and Holmes but also nearly anyone who has taught or published in dynamical systems is now accessible using neural networks. The goal has always been to showcase a suite of computational methods that can be combined with analysis to better understand the increasingly complicated models and datasets that describe our complex world. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2024/1.9781611978162/1.9781611978162/2024101401/1.9781611978162.cover.jpg" alttext="cover image"/></p>
DataDriven Methods for Dynamic Systems
doi:10.1137/1.9781611978162
Jason J. Bramburger
DataDriven Methods for Dynamic Systems
20241014T07:12:40Z
10.1137/1.9781611978162
https://epubs.siam.org/doi/book/10.1137/1.9781611978162?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.

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.

Design of DelayBased Controllers for Linear TimeInvariant Systems
https://epubs.siam.org/doi/book/10.1137/1.9781611978148?af=R
Design of DelayBased Controllers for Linear TimeInvariant Systems. <br/> This book provides the mathematical foundations needed for designing practical controllers for linear timeinvariant systems. The authors accomplish this by incorporating intentional time delays into measurements with the goal of achieving anticipation capabilities, reduction in noise sensitivity, and a fast response. The benefits of these types of delaybased controllers have long been recognized, but designing them based on an analytical approach become possible only recently.
Design of DelayBased Controllers for Linear TimeInvariant Systems. <br/> This book provides the mathematical foundations needed for designing practical controllers for linear timeinvariant systems. The authors accomplish this by incorporating intentional time delays into measurements with the goal of achieving anticipation capabilities, reduction in noise sensitivity, and a fast response. The benefits of these types of delaybased controllers have long been recognized, but designing them based on an analytical approach become possible only recently. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/dc/2024/1.9781611978148/1.9781611978148/20240917/1.9781611978148.cover.jpg" alttext="cover image"/></p>
Design of DelayBased Controllers for Linear TimeInvariant Systems
doi:10.1137/1.9781611978148
Adrián Ramírez
Rifat Sipahi
Sabine Mondié
Rubén Garrido
Design of DelayBased Controllers for Linear TimeInvariant Systems
20240917T04:01:45Z
10.1137/1.9781611978148
https://epubs.siam.org/doi/book/10.1137/1.9781611978148?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.

Numerical Mathematics
https://epubs.siam.org/doi/book/10.1137/1.9781611978070?af=R
Numerical Mathematics. <br/> Excerpt This book is intended as an introduction, at the advanced undergraduate or beginning graduate level, to the mathematics behind practical computational methods for approximating solutions to common problems arising in calculus, differential equations, and linear algebra. The text has developed out of courses I have taught on these topics over the past 10+ years at the University of Kentucky and Portland State University. These courses have always included a mixture of students majoring in mathematics, computer science, and a variety of disciplines within engineering, and this book is written with such an audience in mind. A heavier emphasis has been put on the theory supporting the numerical methods under consideration than is typical in introductory texts (at the advanced undergraduate level) on these topics, but this has not been done at the expense of actual computations. Theory, implementation, and experimentation are all essential to a proper understanding of the subject, and I have tried to strike a good balance between them in the main text and exercises. Supplementary material, including example code and PDF slides containing many of the figures and tables in the text, can be found at https://bookstore.siam.org/ot198/bonus.
Numerical Mathematics. <br/> Excerpt This book is intended as an introduction, at the advanced undergraduate or beginning graduate level, to the mathematics behind practical computational methods for approximating solutions to common problems arising in calculus, differential equations, and linear algebra. The text has developed out of courses I have taught on these topics over the past 10+ years at the University of Kentucky and Portland State University. These courses have always included a mixture of students majoring in mathematics, computer science, and a variety of disciplines within engineering, and this book is written with such an audience in mind. A heavier emphasis has been put on the theory supporting the numerical methods under consideration than is typical in introductory texts (at the advanced undergraduate level) on these topics, but this has not been done at the expense of actual computations. Theory, implementation, and experimentation are all essential to a proper understanding of the subject, and I have tried to strike a good balance between them in the main text and exercises. Supplementary material, including example code and PDF slides containing many of the figures and tables in the text, can be found at https://bookstore.siam.org/ot198/bonus. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2024/1.9781611978070/1.9781611978070/20240912/1.9781611978070.cover.jpg" alttext="cover image"/></p>
Numerical Mathematics
doi:10.1137/1.9781611978070
Jeffrey Ovall
Numerical Mathematics
20240912T02:56:35Z
10.1137/1.9781611978070
https://epubs.siam.org/doi/book/10.1137/1.9781611978070?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.

Numerical Methods for Least Squares Problems: Second Edition
https://epubs.siam.org/doi/book/10.1137/1.9781611977950?af=R
Numerical Methods for Least Squares Problems: Second Edition. <br/> Excerpt More than 25 years have passed since the first edition of this book was published in 1996. Least squares and leastnorm problems have become more significant with every passing decade, and applications have grown in size, complexity, and variety. More advanced techniques for data acquisition give larger amounts of data to be treated. What counts as a large matrix has gone from dimension 1000 to 106. Hence, iterative methods play an increasingly crucial role for the solution of least squares problems. On top of these changes, methods must be adapted to new generations of multiprocessing hardware.
Numerical Methods for Least Squares Problems: Second Edition. <br/> Excerpt More than 25 years have passed since the first edition of this book was published in 1996. Least squares and leastnorm problems have become more significant with every passing decade, and applications have grown in size, complexity, and variety. More advanced techniques for data acquisition give larger amounts of data to be treated. What counts as a large matrix has gone from dimension 1000 to 106. Hence, iterative methods play an increasingly crucial role for the solution of least squares problems. On top of these changes, methods must be adapted to new generations of multiprocessing hardware. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2024/1.9781611977950/1.9781611977950/20240409/1.9781611977950.cover.jpg" alttext="cover image"/></p>
Numerical Methods for Least Squares Problems: Second Edition
doi:10.1137/1.9781611977950
Åke Björck
Numerical Methods for Least Squares Problems: Second Edition
20240409T02:33:49Z
10.1137/1.9781611977950
https://epubs.siam.org/doi/book/10.1137/1.9781611977950?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.

SetValued, Convex, and Nonsmooth Analysis in Dynamics and Control: An Introduction
https://epubs.siam.org/doi/book/10.1137/1.9781611977981?af=R
SetValued, Convex, and Nonsmooth Analysis in Dynamics and Control: An Introduction. <br/> Excerpt This book introduces elements of setvalued analysis, convex analysis, and nonsmooth analysis — which are relatively modern branches of mathematical analysis — and highlights their relevance for and applications to the analysis of dynamical systems, especially those that arise or are of interest in control theory and control engineering.
SetValued, Convex, and Nonsmooth Analysis in Dynamics and Control: An Introduction. <br/> Excerpt This book introduces elements of setvalued analysis, convex analysis, and nonsmooth analysis — which are relatively modern branches of mathematical analysis — and highlights their relevance for and applications to the analysis of dynamical systems, especially those that arise or are of interest in control theory and control engineering. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2024/1.9781611977981/1.9781611977981/20240409/1.9781611977981.cover.jpg" alttext="cover image"/></p>
SetValued, Convex, and Nonsmooth Analysis in Dynamics and Control: An Introduction
doi:10.1137/1.9781611977981
Rafal K. Goebel
SetValued, Convex, and Nonsmooth Analysis in Dynamics and Control: An Introduction
20240409T02:34:19Z
10.1137/1.9781611977981
https://epubs.siam.org/doi/book/10.1137/1.9781611977981?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.

Machine Learning for Asset Management and Pricing
https://epubs.siam.org/doi/book/10.1137/1.9781611977905?af=R
Machine Learning for Asset Management and Pricing. <br/> This textbook covers various machine learning methods applied to asset and liability management, as well as asset pricing. We shortened the title to Machine Learning for Asset Management and Pricing for practical reasons, but also more fundamental ones. First, we do not give much space to liabilities in this book. It would not render justice to the field of asset and liability management (ALM) to include liabilities in the title. It is, however, important for a student to realize that the comprehensive problem of ALM can be handled (at least in theory) using the same theories and methods as asset management or liability management.
Machine Learning for Asset Management and Pricing. <br/> This textbook covers various machine learning methods applied to asset and liability management, as well as asset pricing. We shortened the title to Machine Learning for Asset Management and Pricing for practical reasons, but also more fundamental ones. First, we do not give much space to liabilities in this book. It would not render justice to the field of asset and liability management (ALM) to include liabilities in the title. It is, however, important for a student to realize that the comprehensive problem of ALM can be handled (at least in theory) using the same theories and methods as asset management or liability management. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2024/1.9781611977905/1.9781611977905/20240305/1.9781611977905.cover.jpg" alttext="cover image"/></p>
Machine Learning for Asset Management and Pricing
doi:10.1137/1.9781611977905
Henry Schellhorn
Tianmin Kong
Machine Learning for Asset Management and Pricing
20240305T10:32:55Z
10.1137/1.9781611977905
https://epubs.siam.org/doi/book/10.1137/1.9781611977905?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

Industrial Mathematics: The 1998 CRSC Workshop
https://epubs.siam.org/doi/book/10.1137/1.9780898714678?af=R
Industrial Mathematics: The 1998 CRSC Workshop. <br/>
Industrial Mathematics: The 1998 CRSC Workshop. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2000/1.9780898714678/1.9780898714678/20231212/1.9780898714678.cover.jpg" alttext="cover image"/></p>
Industrial Mathematics: The 1998 CRSC Workshop
doi:10.1137/1.9780898714678
Industrial Mathematics: The 1998 CRSC Workshop
20231212T01:47:25Z
10.1137/1.9780898714678
https://epubs.siam.org/doi/book/10.1137/1.9780898714678?af=R
© 2000 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.

Problems and Solutions for Integer and Combinatorial Optimization: Building Skills in Discrete Optimization
https://epubs.siam.org/doi/book/10.1137/1.9781611977769?af=R
Problems and Solutions for Integer and Combinatorial Optimization: Building Skills in Discrete Optimization. <br/> The authors of this book wanted for a long time to have at hand a large number of problems with solutions while teaching. The first author has taught IE303 Modeling and Methods in Optimization for Bilkent University students for longer than 20 years. A graduate student (a PhD candidate) assistant joined next and started compiling solved problems to be used in exams, quizzes, and homework assignments. The first author added some problems of his own. The result is this book. There are not many books out there dedicated to problems in integer optimization and related topics. The book focuses on the topics covered in IE303 Modeling and Methods in Optimization, a third year required course for Industrial Engineering students at Bilkent University. However, it should be useful for any undergraduate student in industrial engineering or in related disciplines. These are the motivations behind the preparation of this book.
Problems and Solutions for Integer and Combinatorial Optimization: Building Skills in Discrete Optimization. <br/> The authors of this book wanted for a long time to have at hand a large number of problems with solutions while teaching. The first author has taught IE303 Modeling and Methods in Optimization for Bilkent University students for longer than 20 years. A graduate student (a PhD candidate) assistant joined next and started compiling solved problems to be used in exams, quizzes, and homework assignments. The first author added some problems of his own. The result is this book. There are not many books out there dedicated to problems in integer optimization and related topics. The book focuses on the topics covered in IE303 Modeling and Methods in Optimization, a third year required course for Industrial Engineering students at Bilkent University. However, it should be useful for any undergraduate student in industrial engineering or in related disciplines. These are the motivations behind the preparation of this book. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mo/2023/1.9781611977769/1.9781611977769/20231113/1.9781611977769.cover.jpg" alttext="cover image"/></p>
Problems and Solutions for Integer and Combinatorial Optimization: Building Skills in Discrete Optimization
doi:10.1137/1.9781611977769
Mustafa Ç. Pınar
Deniz Akkaya
Problems and Solutions for Integer and Combinatorial Optimization: Building Skills in Discrete Optimization
20231113T01:07:28Z
10.1137/1.9781611977769
https://epubs.siam.org/doi/book/10.1137/1.9781611977769?af=R
© 2023 by the Society for Industrial and Applied Mathematics

Linear and Nonlinear Optimization 2nd Edition
https://epubs.siam.org/doi/book/10.1137/1.9780898717730?af=R
Linear and Nonlinear Optimization 2nd Edition. <br/>
Linear and Nonlinear Optimization 2nd Edition. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2008/1.9780898717730/1.9780898717730/20230629/1.9780898717730.cover.jpg" alttext="cover image"/></p>
Linear and Nonlinear Optimization 2nd Edition
doi:10.1137/1.9780898717730
Igor Griva
Stephen G. Nash
Ariela Sofer
Linear and Nonlinear Optimization 2nd Edition
20230629T06:55:40Z
10.1137/1.9780898717730
https://epubs.siam.org/doi/book/10.1137/1.9780898717730?af=R
© 2008 by the Society for Industrial and Applied Mathematics

Matrix Analysis for Scientists and Engineers
https://epubs.siam.org/doi/book/10.1137/1.9780898717907?af=R
Matrix Analysis for Scientists and Engineers. <br/>
Matrix Analysis for Scientists and Engineers. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2004/1.9780898717907/1.9780898717907/20230629/1.9780898717907.cover.jpg" alttext="cover image"/></p>
Matrix Analysis for Scientists and Engineers
doi:10.1137/1.9780898717907
Alan J. Laub
Matrix Analysis for Scientists and Engineers
20230629T06:53:40Z
10.1137/1.9780898717907
https://epubs.siam.org/doi/book/10.1137/1.9780898717907?af=R
© 2005 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.

Least Squares Data Fitting with Applications
https://epubs.siam.org/doi/book/10.1137/1.9781421407869?af=R
Least Squares Data Fitting with Applications. <br/>
Least Squares Data Fitting with Applications. <br/><p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/jh/2013/1.9781421407869/1.9781421407869/20230629/1.9781421407869.cover.jpg" alttext="cover image"/></p>
Least Squares Data Fitting with Applications
doi:10.1137/1.9781421407869
Per Christian Hansen
Victor Pereyra
Goldela Scherer
Least Squares Data Fitting with Applications
20230629T02:43:37Z
10.1137/1.9781421407869
https://epubs.siam.org/doi/book/10.1137/1.9781421407869?af=R
© 2013 by the Johns Hopkins University Press

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 and Tools for Financial Engineering
https://epubs.siam.org/doi/book/10.1137/1.9781611976762?af=R
Mathematics and Tools for Financial Engineering. <br/> Investment strategies are becoming more sophisticated due to theoretical developments in finance coupled with advanced software tools and fast computational capabilities and due to the increasing number of financial investment options that are available. The traditional way of solving financial problems and making investment decisions has been replaced with intelligent techniques that involve good understanding of the theory for modeling the dynamic behavior of assets and investments, optimization techniques to choose the best solution from a set of many feasible solutions, and software tools to simulate the theory and generate solutions, evaluate performance and risks, and make predictions.
Mathematics and Tools for Financial Engineering. <br/> Investment strategies are becoming more sophisticated due to theoretical developments in finance coupled with advanced software tools and fast computational capabilities and due to the increasing number of financial investment options that are available. The traditional way of solving financial problems and making investment decisions has been replaced with intelligent techniques that involve good understanding of the theory for modeling the dynamic behavior of assets and investments, optimization techniques to choose the best solution from a set of many feasible solutions, and software tools to simulate the theory and generate solutions, evaluate performance and risks, and make predictions. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2021/1.9781611976762/1.9781611976762/20230629/1.9781611976762.cover.jpg" alttext="cover image"/></p>
Mathematics and Tools for Financial Engineering
doi:10.1137/1.9781611976762
Petros A. Ioannou
Mathematics and Tools for Financial Engineering
20230629T08:28:00Z
10.1137/1.9781611976762
https://epubs.siam.org/doi/book/10.1137/1.9781611976762?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.

Applied Numerical Linear Algebra
https://epubs.siam.org/doi/book/10.1137/1.9781611976861?af=R
Applied Numerical Linear Algebra. <br/> The original edition of Applied Numerical Linear Algebra emerged from a numerical linear algebra course first taught more than 40 years ago at Carnegie Mellon University and later at Penn State University. The course was targeted to junior and senior undergraduate students and beginning graduate students. Much has changed since then: there have been huge advances in computer speed and algorithms; the development of MATLAB has made it much easier to formulate and solve complex problems; advances in sparse matrix theory have enabled the direct solution of increasingly large linear systems; iterative methods have greatly advanced through the development of MINRES (minimal residual) and GMRES (generalized minimal residual) algorithms; and a solid mathematical foundation for analyzing error propagation in finite precision arithmetic has been achieved. Without a doubt, hardware, software, algorithms, and theory will continue to develop.
Applied Numerical Linear Algebra. <br/> The original edition of Applied Numerical Linear Algebra emerged from a numerical linear algebra course first taught more than 40 years ago at Carnegie Mellon University and later at Penn State University. The course was targeted to junior and senior undergraduate students and beginning graduate students. Much has changed since then: there have been huge advances in computer speed and algorithms; the development of MATLAB has made it much easier to formulate and solve complex problems; advances in sparse matrix theory have enabled the direct solution of increasingly large linear systems; iterative methods have greatly advanced through the development of MINRES (minimal residual) and GMRES (generalized minimal residual) algorithms; and a solid mathematical foundation for analyzing error propagation in finite precision arithmetic has been achieved. Without a doubt, hardware, software, algorithms, and theory will continue to develop. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/cl/2021/1.9781611976861/1.9781611976861/20230629/1.9781611976861.cover.jpg" alttext="cover image"/></p>
Applied Numerical Linear Algebra
doi:10.1137/1.9781611976861
William W. Hager
Applied Numerical Linear Algebra
20230629T08:28:40Z
10.1137/1.9781611976861
https://epubs.siam.org/doi/book/10.1137/1.9781611976861?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.

Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia
https://epubs.siam.org/doi/book/10.1137/1.9781611977271?af=R
Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia. <br/> This book on solvers for nonlinear equations is a useroriented guide to algorithms and implementation. It is a sequel to [111], which used MATLAB for the solvers and examples. This book uses Julia [17] and adds new material on pseudotransient continuation, mixed precision solvers, and Anderson acceleration.
Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia. <br/> This book on solvers for nonlinear equations is a useroriented guide to algorithms and implementation. It is a sequel to [111], which used MATLAB for the solvers and examples. This book uses Julia [17] and adds new material on pseudotransient continuation, mixed precision solvers, and Anderson acceleration. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/fa/2022/1.9781611977271/1.9781611977271/20230629/1.9781611977271.cover.gif" alttext="cover image"/></p>
Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia
doi:10.1137/1.9781611977271
C. T. Kelley
Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia
20230629T08:30:40Z
10.1137/1.9781611977271
https://epubs.siam.org/doi/book/10.1137/1.9781611977271?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 Basics of Practical Optimization, Second Edition
https://epubs.siam.org/doi/book/10.1137/1.9781611977370?af=R
The Basics of Practical Optimization, Second Edition. <br/> Optimization is presented in most multivariable calculus courses as an application of the gradient, and while this treatment makes sense for a calculus course, there is much more to the theory of optimization. Moreover, optimization is actually used every day in a way that is much different from what one is led to believe in the typical calculus course. Our world and its societies have for many centuries generated interesting and important optimization problems, and the theory of optimization has grown and developed in response to the challenges presented by these problems. In fact, optimization theory continues to be developed today in response to practical concerns encountered in applications, which makes optimization an ideal topic of study in modern applied mathematics. Through the study of optimization theory, the power and beauty of mathematics can be observed in close connection to interesting and relevant problems of our world.
The Basics of Practical Optimization, Second Edition. <br/> Optimization is presented in most multivariable calculus courses as an application of the gradient, and while this treatment makes sense for a calculus course, there is much more to the theory of optimization. Moreover, optimization is actually used every day in a way that is much different from what one is led to believe in the typical calculus course. Our world and its societies have for many centuries generated interesting and important optimization problems, and the theory of optimization has grown and developed in response to the challenges presented by these problems. In fact, optimization theory continues to be developed today in response to practical concerns encountered in applications, which makes optimization an ideal topic of study in modern applied mathematics. Through the study of optimization theory, the power and beauty of mathematics can be observed in close connection to interesting and relevant problems of our world. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/ot/2022/1.9781611977370/1.9781611977370/20230629/1.9781611977370.cover.jpg" alttext="cover image"/></p>
The Basics of Practical Optimization, Second Edition
doi:10.1137/1.9781611977370
Adam B. Levy
The Basics of Practical Optimization, Second Edition
20230629T08:31:20Z
10.1137/1.9781611977370
https://epubs.siam.org/doi/book/10.1137/1.9781611977370?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

Moment and Polynomial Optimization
https://epubs.siam.org/doi/book/10.1137/1.9781611977608?af=R
Moment and Polynomial Optimization. <br/> Moment and polynomial optimization has received high attention in recent decades. It has beautiful theory and efficient methods, as well as broad applications for various mathematical, scientific, and engineering fields. The research status of optimization has been enhanced extensively due to its recent developments. Nowadays, moment and polynomial optimization is an important technique in many fields.
Moment and Polynomial Optimization. <br/> Moment and polynomial optimization has received high attention in recent decades. It has beautiful theory and efficient methods, as well as broad applications for various mathematical, scientific, and engineering fields. The research status of optimization has been enhanced extensively due to its recent developments. Nowadays, moment and polynomial optimization is an important technique in many fields. <p><img src="https://epubs.siam.org/na101/home/literatum/publisher/siam/books/content/mo/2023/1.9781611977608/1.9781611977608/20230622/1.9781611977608.cover.jpg" alttext="cover image"/></p>
Moment and Polynomial Optimization
doi:10.1137/1.9781611977608
Jiawang Nie
Moment and Polynomial Optimization
20230622T07:07:58Z
10.1137/1.9781611977608
https://epubs.siam.org/doi/book/10.1137/1.9781611977608?af=R
© 2023 by the Society for Industrial and Applied Mathematics