Society for Industrial and Applied Mathematics: Keyword search for query
Keyword search result for All Books  New results matching your keyword search query (updated daily).
https://epubs.siam.org/action/doSearch?type=advanced&mi=9qwetu&af=R&pubType=book&target=browse
Society for Industrial and Applied Mathematics: Keyword search for query
Society for Industrial and Applied Mathematics
enUS
Atypon Systems
http://www.atypon.com/images/atypon_logo_small.gif
http://www.atypon.com

Sparse Polynomial Approximation of HighDimensional Functions
https://epubs.siam.org/doi/book/10.1137/1.9781611976885?mi=9qwetu&af=R&pubType=book&target=browse
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.
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?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Iterative Methods and Preconditioners for Systems of Linear Equations
https://epubs.siam.org/doi/book/10.1137/1.9781611976908?mi=9qwetu&af=R&pubType=book&target=browse
Iterative Methods and Preconditioners for Systems of Linear Equations. <br/> This book gives an introduction to iterative methods and preconditioning for solving discretized elliptic partial differential equations (PDEs) and optimal control problems governed by elliptic PDEs, for which the use of matrixfree procedures is crucial. It grew out of a set of lecture notes going back to the Ph.D. days of the second author at Stanford, prepared for CS137. The lecture notes evolved through lectures given at McGill University and the University of Geneva by the second author, where the topic became a specialized advanced undergraduate/early graduate course. In 2017, when the first author was a teaching assistant in Geneva, the lecture notes were restructured, expanded, and enriched into an earlier form of the book, without optimal control yet. After that, the same subject was taught again (and in several different forms) by the second author at the University of Geneva and the first author as professor at the University of Konstanz. These last years allowed the authors to further improve the manuscript, further enrich it with several examples, and add new insights, new sections, and the new chapter about optimal control problems.
Iterative Methods and Preconditioners for Systems of Linear Equations. <br/> This book gives an introduction to iterative methods and preconditioning for solving discretized elliptic partial differential equations (PDEs) and optimal control problems governed by elliptic PDEs, for which the use of matrixfree procedures is crucial. It grew out of a set of lecture notes going back to the Ph.D. days of the second author at Stanford, prepared for CS137. The lecture notes evolved through lectures given at McGill University and the University of Geneva by the second author, where the topic became a specialized advanced undergraduate/early graduate course. In 2017, when the first author was a teaching assistant in Geneva, the lecture notes were restructured, expanded, and enriched into an earlier form of the book, without optimal control yet. After that, the same subject was taught again (and in several different forms) by the second author at the University of Geneva and the first author as professor at the University of Konstanz. These last years allowed the authors to further improve the manuscript, further enrich it with several examples, and add new insights, new sections, and the new chapter about optimal control problems.
Iterative Methods and Preconditioners for Systems of Linear Equations
doi:10.1137/1.9781611976908
Gabriele Ciaramella
Martin J. Gander
Iterative Methods and Preconditioners for Systems of Linear Equations
20220208T09:20:11Z
10.1137/1.9781611976908
https://epubs.siam.org/doi/book/10.1137/1.9781611976908?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Transfinite Interpolations and Eulerian/Lagrangian Dynamics
https://epubs.siam.org/doi/book/10.1137/1.9781611976953?mi=9qwetu&af=R&pubType=book&target=browse
Transfinite Interpolations and Eulerian/Lagrangian Dynamics. <br/> A Most Ingenious, Useful, and Beautiful Subject Continuous interpolation of a function from its values at a finite number of points is a fundamental tool in numerical analysis and statistics. For instance, the celebrated Lagrange interpolation was discovered in 1779 by E. Waring [1] but named after Lagrange in 1795.1
Transfinite Interpolations and Eulerian/Lagrangian Dynamics. <br/> A Most Ingenious, Useful, and Beautiful Subject Continuous interpolation of a function from its values at a finite number of points is a fundamental tool in numerical analysis and statistics. For instance, the celebrated Lagrange interpolation was discovered in 1779 by E. Waring [1] but named after Lagrange in 1795.1
Transfinite Interpolations and Eulerian/Lagrangian Dynamics
doi:10.1137/1.9781611976953
André Garon
Michel C. Delfour
Transfinite Interpolations and Eulerian/Lagrangian Dynamics
20220325T08:44:49Z
10.1137/1.9781611976953
https://epubs.siam.org/doi/book/10.1137/1.9781611976953?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Lectures on Stochastic Programming: Modeling and Theory, Third Edition
https://epubs.siam.org/doi/book/10.1137/1.9781611976595?mi=9qwetu&af=R&pubType=book&target=browse
Lectures on Stochastic Programming: Modeling and Theory, Third Edition. <br/> This is a substantial revision of the previous edition with added new material. The presentation of Chapter 6 is updated. In particular the Interchangeability Principle for risk measures is discussed in detail. Two new chapters are added. In Chapter 7 we present a systematic theory of distributionally robust stochastic programming (DRSP). Currently this is a hot topic of research. Particular attention is given to mathematical foundations of multistage formulations of DRSP. Statistical properties of empirical estimates of distributionally robust functionals are discussed. Time consistency of multistage problems is formulated in a general framework of preference systems with a particular application to distributionally robust stopping time problems. In Chapter 8 there is new material on formulation and numerical approaches to solving periodical multistage stochastic programs.
Lectures on Stochastic Programming: Modeling and Theory, Third Edition. <br/> This is a substantial revision of the previous edition with added new material. The presentation of Chapter 6 is updated. In particular the Interchangeability Principle for risk measures is discussed in detail. Two new chapters are added. In Chapter 7 we present a systematic theory of distributionally robust stochastic programming (DRSP). Currently this is a hot topic of research. Particular attention is given to mathematical foundations of multistage formulations of DRSP. Statistical properties of empirical estimates of distributionally robust functionals are discussed. Time consistency of multistage problems is formulated in a general framework of preference systems with a particular application to distributionally robust stopping time problems. In Chapter 8 there is new material on formulation and numerical approaches to solving periodical multistage stochastic programs.
Lectures on Stochastic Programming: Modeling and Theory, Third Edition
doi:10.1137/1.9781611976595
Alexander Shapiro
Darinka Dentcheva
Andrzej Ruszczynski
Lectures on Stochastic Programming: Modeling and Theory, Third Edition
20210820T01:20:53Z
10.1137/1.9781611976595
https://epubs.siam.org/doi/book/10.1137/1.9781611976595?mi=9qwetu&af=R&pubType=book&target=browse
© 2021 by the Society for Industrial and Applied Mathematics and the Mathematical Optimization SocietyAll 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 Solutions of Underdetermined Linear Systems and Their Applications
https://epubs.siam.org/doi/book/10.1137/1.9781611976519?mi=9qwetu&af=R&pubType=book&target=browse
Sparse Solutions of Underdetermined Linear Systems and Their Applications. <br/> A linear system of equations consists of a known matrix A and a known vector b such that Ax = b for an unknown vector x, where A is of size m × n. When m = n and m > n, methods of solving for x are well known and are discussed in standard numerical analysis textbooks. However, when m < n, Ax = b is called an underdetermined linear system. Such a linear system has become a subject of research in the last 15 years as part of an extremely active study in the community of compressive sensing. Mathematically, an underdetermined linear system has many solutions. The aim of the study is to find a sparse solution in the sense that the number of nonzero entries of the solution is the smallest. Although there exists a sparse solution, the difficulty is that it may take years to find such an exact solution using our current computer power. Therefore, we must look for an approximation of the sparse solution which can be found within reasonable time and tolerance. Compressed sensing and sparse solutions of underdetermined linear system have been actively studied in many branches of mathematics, e.g., approximation theory, Banach space theory, combinatorics, discrete and continuous optimization, numerical analysis, and probability, as well as in many other sciences, e.g., computer science, electrical engineering, medical sciences, optimization, and statistics.
Sparse Solutions of Underdetermined Linear Systems and Their Applications. <br/> A linear system of equations consists of a known matrix A and a known vector b such that Ax = b for an unknown vector x, where A is of size m × n. When m = n and m > n, methods of solving for x are well known and are discussed in standard numerical analysis textbooks. However, when m < n, Ax = b is called an underdetermined linear system. Such a linear system has become a subject of research in the last 15 years as part of an extremely active study in the community of compressive sensing. Mathematically, an underdetermined linear system has many solutions. The aim of the study is to find a sparse solution in the sense that the number of nonzero entries of the solution is the smallest. Although there exists a sparse solution, the difficulty is that it may take years to find such an exact solution using our current computer power. Therefore, we must look for an approximation of the sparse solution which can be found within reasonable time and tolerance. Compressed sensing and sparse solutions of underdetermined linear system have been actively studied in many branches of mathematics, e.g., approximation theory, Banach space theory, combinatorics, discrete and continuous optimization, numerical analysis, and probability, as well as in many other sciences, e.g., computer science, electrical engineering, medical sciences, optimization, and statistics.
Sparse Solutions of Underdetermined Linear Systems and Their Applications
doi:10.1137/1.9781611976519
MingJun Lai
Yang Wang
Sparse Solutions of Underdetermined Linear Systems and Their Applications
20210617T07:16:25Z
10.1137/1.9781611976519
https://epubs.siam.org/doi/book/10.1137/1.9781611976519?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones
https://epubs.siam.org/doi/book/10.1137/1.9781611976533?mi=9qwetu&af=R&pubType=book&target=browse
Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones. <br/> In recent years, the world of molecular biology has witnessed a technology explosion, the socalled omics revolution, which has opened windows into cellular metabolic potential and activity that have already revolutionized numerous subfields of biology and medicine. At the same time, this technology (genomics, transcriptomics, etc.), intrinsically through its very nature and extrinsically through its widespread use, has generated a lot of data that begs, and has received, much statistical and mathematical attention. Such data analysis is arguably as important, if not more so, than the measurements themselves; without the appropriate parsing, much of the information potential is inaccessible.
Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones. <br/> In recent years, the world of molecular biology has witnessed a technology explosion, the socalled omics revolution, which has opened windows into cellular metabolic potential and activity that have already revolutionized numerous subfields of biology and medicine. At the same time, this technology (genomics, transcriptomics, etc.), intrinsically through its very nature and extrinsically through its widespread use, has generated a lot of data that begs, and has received, much statistical and mathematical attention. Such data analysis is arguably as important, if not more so, than the measurements themselves; without the appropriate parsing, much of the information potential is inaccessible.
Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones
doi:10.1137/1.9781611976533
Isaac Klapper
Daniel B. Szyld
Metabolic Networks, Elementary Flux Modes, and Polyhedral Cones
20210617T07:46:43Z
10.1137/1.9781611976533
https://epubs.siam.org/doi/book/10.1137/1.9781611976533?mi=9qwetu&af=R&pubType=book&target=browse
© 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 and Optimization
https://epubs.siam.org/doi/book/10.1137/1.9781611976571?mi=9qwetu&af=R&pubType=book&target=browse
Numerical Linear Algebra and Optimization. <br/> Methods for solving problems in science, engineering, medicine, and business rely on results and algorithms from linear algebra and optimization—two areas that are very close scientifically. When we wrote this book in 1990, textbooks that treated them together were rare. To try to improve that situation, Numerical Linear Algebra and Optimization was structured to present, from a unified perspective, the major overlapping content, namely linear systems, linear leastsquares, and linear programming (optimization of a linear objective function subject to linear constraints). Our motivation throughout was to emphasize the ties between linear algebra and optimization as much as possible.
Numerical Linear Algebra and Optimization. <br/> Methods for solving problems in science, engineering, medicine, and business rely on results and algorithms from linear algebra and optimization—two areas that are very close scientifically. When we wrote this book in 1990, textbooks that treated them together were rare. To try to improve that situation, Numerical Linear Algebra and Optimization was structured to present, from a unified perspective, the major overlapping content, namely linear systems, linear leastsquares, and linear programming (optimization of a linear objective function subject to linear constraints). Our motivation throughout was to emphasize the ties between linear algebra and optimization as much as possible.
Numerical Linear Algebra and Optimization
doi:10.1137/1.9781611976571
Philip E. Gill
Walter Murray
Margaret H. Wright
Numerical Linear Algebra and Optimization
20210617T07:14:43Z
10.1137/1.9781611976571
https://epubs.siam.org/doi/book/10.1137/1.9781611976571?mi=9qwetu&af=R&pubType=book&target=browse
© 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 Continuation and Bifurcation in Nonlinear PDEs
https://epubs.siam.org/doi/book/10.1137/1.9781611976618?mi=9qwetu&af=R&pubType=book&target=browse
Numerical Continuation and Bifurcation in Nonlinear PDEs. <br/> In this book we consider solution branches and bifurcations in nonlinear partial differential equations (PDEs) as models from science (and some economics). Given a nonlinear PDE, four basic questions are the following: Do (locally unique?) solutions exist? What are general properties of solutions? How can solutions be effectively computed (approximated)? What do we learn from the solutions about Nature?
Numerical Continuation and Bifurcation in Nonlinear PDEs. <br/> In this book we consider solution branches and bifurcations in nonlinear partial differential equations (PDEs) as models from science (and some economics). Given a nonlinear PDE, four basic questions are the following: Do (locally unique?) solutions exist? What are general properties of solutions? How can solutions be effectively computed (approximated)? What do we learn from the solutions about Nature?
Numerical Continuation and Bifurcation in Nonlinear PDEs
doi:10.1137/1.9781611976618
Hannes Uecker
Numerical Continuation and Bifurcation in Nonlinear PDEs
20210820T01:23:14Z
10.1137/1.9781611976618
https://epubs.siam.org/doi/book/10.1137/1.9781611976618?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Matrix Analysis and Computations
https://epubs.siam.org/doi/book/10.1137/1.9781611976632?mi=9qwetu&af=R&pubType=book&target=browse
Matrix Analysis and Computations. <br/> Analysis and computation, the most important and indispensable methods for processing matrices, are closely related but two significantly different areas. The former focuses more on theoretical analysis and belongs to the category of pure linear algebra, while the latter concentrates more on practical applications and is classified as numerical linear algebra, although both of them are located at the core bases of pure and computational mathematics. Moreover, matrix theory is the kernel and foundation of matrix computations, while matrix computations are the extensions and applications of matrix theory. Therefore, these two kinds of knowledge are absolute requirements for matrix analysts, computational scientists, and algorithmic practitioners.
Matrix Analysis and Computations. <br/> Analysis and computation, the most important and indispensable methods for processing matrices, are closely related but two significantly different areas. The former focuses more on theoretical analysis and belongs to the category of pure linear algebra, while the latter concentrates more on practical applications and is classified as numerical linear algebra, although both of them are located at the core bases of pure and computational mathematics. Moreover, matrix theory is the kernel and foundation of matrix computations, while matrix computations are the extensions and applications of matrix theory. Therefore, these two kinds of knowledge are absolute requirements for matrix analysts, computational scientists, and algorithmic practitioners.
Matrix Analysis and Computations
doi:10.1137/1.9781611976632
ZhongZhi Bai
JianYu Pan
Matrix Analysis and Computations
20210909T07:40:17Z
10.1137/1.9781611976632
https://epubs.siam.org/doi/book/10.1137/1.9781611976632?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Locally Convex Spaces and Harmonic Analysis: An Introduction
https://epubs.siam.org/doi/book/10.1137/1.9781611976656?mi=9qwetu&af=R&pubType=book&target=browse
Locally Convex Spaces and Harmonic Analysis: An Introduction. <br/> This textbook is intended to serve three purposes.
Locally Convex Spaces and Harmonic Analysis: An Introduction. <br/> This textbook is intended to serve three purposes.
Locally Convex Spaces and Harmonic Analysis: An Introduction
doi:10.1137/1.9781611976656
Philippe Ciarlet
Locally Convex Spaces and Harmonic Analysis: An Introduction
20210810T02:49:44Z
10.1137/1.9781611976656
https://epubs.siam.org/doi/book/10.1137/1.9781611976656?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Computed Tomography: Algorithms, Insight, and Just Enough Theory
https://epubs.siam.org/doi/book/10.1137/1.9781611976670?mi=9qwetu&af=R&pubType=book&target=browse
Computed Tomography: Algorithms, Insight, and Just Enough Theory. <br/> This book is primarily aimed at students, researchers, and practitioners who are interested in the computational aspects of Xray computed tomography (CT). It is also relevant for those doing other forms of tomography, such as neutron and electron tomography, that have the same mathematical formulation.
Computed Tomography: Algorithms, Insight, and Just Enough Theory. <br/> This book is primarily aimed at students, researchers, and practitioners who are interested in the computational aspects of Xray computed tomography (CT). It is also relevant for those doing other forms of tomography, such as neutron and electron tomography, that have the same mathematical formulation.
Computed Tomography: Algorithms, Insight, and Just Enough Theory
doi:10.1137/1.9781611976670
Computed Tomography: Algorithms, Insight, and Just Enough Theory
20210927T12:29:44Z
10.1137/1.9781611976670
https://epubs.siam.org/doi/book/10.1137/1.9781611976670?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Basics and Trends in Sensitivity Analysis
https://epubs.siam.org/doi/book/10.1137/1.9781611976694?mi=9qwetu&af=R&pubType=book&target=browse
Basics and Trends in Sensitivity Analysis. <br/> In many fields, such as environmental risk assessment, agronomic system behavior, aerospace engineering, and nuclear safety, mathematical models turned into computer code are used for making predictions and performing simulations when experiments would be too expensive or even infeasible. Modern computer models that simulate physical phenomena often input a high number of numerical parameters and physical variables and provide a number of outputs (scalars or functions). Inputs may include engineering or operating variables, variables that describe field conditions, and variables that include unknown or partially known model parameters.
Basics and Trends in Sensitivity Analysis. <br/> In many fields, such as environmental risk assessment, agronomic system behavior, aerospace engineering, and nuclear safety, mathematical models turned into computer code are used for making predictions and performing simulations when experiments would be too expensive or even infeasible. Modern computer models that simulate physical phenomena often input a high number of numerical parameters and physical variables and provide a number of outputs (scalars or functions). Inputs may include engineering or operating variables, variables that describe field conditions, and variables that include unknown or partially known model parameters.
Basics and Trends in Sensitivity Analysis
doi:10.1137/1.9781611976694
Sébastien Da Veiga
Fabrice Gamboa
Bertrand Iooss
Clémentine Prieur
Basics and Trends in Sensitivity Analysis
20211015T01:34:37Z
10.1137/1.9781611976694
https://epubs.siam.org/doi/book/10.1137/1.9781611976694?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Methods in Computational Science
https://epubs.siam.org/doi/book/10.1137/1.9781611976724?mi=9qwetu&af=R&pubType=book&target=browse
Methods in Computational Science. <br/> Computational methods are today an integral part of most scientific disciplines, and hence a rudimentary understanding of their potential and limitations is essential for any scientist or engineer. This book provides an introduction to computational science through a set of methods and algorithms in the field, with the aim to give the reader both a familiarity with the theoretical foundations as well as practical skills to use and develop computational methods. Some of the methods in this book are at the research frontier, whereas other are well established and available as black box functions, for example, in BLAS libraries, MATLAB toolboxes, R, or Python packages. To develop the skills needed to choose the right methods, and to understand their strengths and weaknesses, I believe it is a more fruitful path to learn the fundamental ideas and principles underlying the methods, rather than learning how to operate the black box functions based on best practices protocols. Therefore, in selecting the material I have focused on the broad foundational themes from mathematics and computer science upon which computational methods are built. This also means that only some of the methods are analyzed in detail, selected based on their significance in modern computational science or their foundational impact on the field.
Methods in Computational Science. <br/> Computational methods are today an integral part of most scientific disciplines, and hence a rudimentary understanding of their potential and limitations is essential for any scientist or engineer. This book provides an introduction to computational science through a set of methods and algorithms in the field, with the aim to give the reader both a familiarity with the theoretical foundations as well as practical skills to use and develop computational methods. Some of the methods in this book are at the research frontier, whereas other are well established and available as black box functions, for example, in BLAS libraries, MATLAB toolboxes, R, or Python packages. To develop the skills needed to choose the right methods, and to understand their strengths and weaknesses, I believe it is a more fruitful path to learn the fundamental ideas and principles underlying the methods, rather than learning how to operate the black box functions based on best practices protocols. Therefore, in selecting the material I have focused on the broad foundational themes from mathematics and computer science upon which computational methods are built. This also means that only some of the methods are analyzed in detail, selected based on their significance in modern computational science or their foundational impact on the field.
Methods in Computational Science
doi:10.1137/1.9781611976724
Johan Hoffman
Methods in Computational Science
20220111T08:59:09Z
10.1137/1.9781611976724
https://epubs.siam.org/doi/book/10.1137/1.9781611976724?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Modern Nonconvex Nondifferentiable Optimization
https://epubs.siam.org/doi/book/10.1137/1.9781611976748?mi=9qwetu&af=R&pubType=book&target=browse
Modern Nonconvex Nondifferentiable Optimization. <br/> Mathematical optimization has always been at the heart of engineering, statistics, and economics. In these applied domains, optimization concepts and methods have often been used without a clear understanding of their limitations and supporting theory. The situation has become increasingly worse as these methods have assumed a central role in the modern era of bigdata science and analytics, where optimizationbased statistical learning is one of the main tools. Yet computational optimization has traditionally been of little concern in statistical analysis.
Modern Nonconvex Nondifferentiable Optimization. <br/> Mathematical optimization has always been at the heart of engineering, statistics, and economics. In these applied domains, optimization concepts and methods have often been used without a clear understanding of their limitations and supporting theory. The situation has become increasingly worse as these methods have assumed a central role in the modern era of bigdata science and analytics, where optimizationbased statistical learning is one of the main tools. Yet computational optimization has traditionally been of little concern in statistical analysis.
Modern Nonconvex Nondifferentiable Optimization
doi:10.1137/1.9781611976748
Ying Cui
JongShi Pang
Modern Nonconvex Nondifferentiable Optimization
20211203T02:35:01Z
10.1137/1.9781611976748
https://epubs.siam.org/doi/book/10.1137/1.9781611976748?mi=9qwetu&af=R&pubType=book&target=browse
© 2021 by the Society for Industrial and Applied Mathematics and the Mathematical Optimization SocietyAll 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.

Mathematical Elasticity: ThreeDimensional Elasticity
https://epubs.siam.org/doi/book/10.1137/1.9781611976786?mi=9qwetu&af=R&pubType=book&target=browse
Mathematical Elasticity: ThreeDimensional Elasticity. <br/> Introduction Since the completion of this volume in 1986, there has been an enormous amount of fundamental advances in the theory of threedimensional elasticity (Chapters 6 and 7 in Volume I), a substantial part of it being a direct consequence of John Ball's landmark paper of 1977. Nevertheless, it is safe to assert that the content of Volume I remains essentially up to date.
Mathematical Elasticity: ThreeDimensional Elasticity. <br/> Introduction Since the completion of this volume in 1986, there has been an enormous amount of fundamental advances in the theory of threedimensional elasticity (Chapters 6 and 7 in Volume I), a substantial part of it being a direct consequence of John Ball's landmark paper of 1977. Nevertheless, it is safe to assert that the content of Volume I remains essentially up to date.
Mathematical Elasticity: ThreeDimensional Elasticity
doi:10.1137/1.9781611976786
Philippe G. Ciarlet
Mathematical Elasticity: ThreeDimensional Elasticity
20220208T07:44:07Z
10.1137/1.9781611976786
https://epubs.siam.org/doi/book/10.1137/1.9781611976786?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Mathematical Elasticity: Theory of Plates
https://epubs.siam.org/doi/book/10.1137/1.9781611976809?mi=9qwetu&af=R&pubType=book&target=browse
Mathematical Elasticity: Theory of Plates. <br/> Introduction The “linear part” of Volume II (Chapters 1 to 3), which covers the rigorous twodimensional modeling of linearly elastic plates and shallow shells, as well as a thorough mathematical analysis of their twodimensional models, remains essentially up to date.
Mathematical Elasticity: Theory of Plates. <br/> Introduction The “linear part” of Volume II (Chapters 1 to 3), which covers the rigorous twodimensional modeling of linearly elastic plates and shallow shells, as well as a thorough mathematical analysis of their twodimensional models, remains essentially up to date.
Mathematical Elasticity: Theory of Plates
doi:10.1137/1.9781611976809
Philippe G. Ciarlet
Mathematical Elasticity: Theory of Plates
20220208T08:10:09Z
10.1137/1.9781611976809
https://epubs.siam.org/doi/book/10.1137/1.9781611976809?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Mathematical Elasticity: Theory of Shells
https://epubs.siam.org/doi/book/10.1137/1.9781611976823?mi=9qwetu&af=R&pubType=book&target=browse
Mathematical Elasticity: Theory of Shells. <br/> Introduction It is not a coincidence that this preface and that of Volume II in some places display strong similarities in their presentation and style; these similarities simply reflect that the applications of Γconvergence theory to plate and shell theories share many resemblances.
Mathematical Elasticity: Theory of Shells. <br/> Introduction It is not a coincidence that this preface and that of Volume II in some places display strong similarities in their presentation and style; these similarities simply reflect that the applications of Γconvergence theory to plate and shell theories share many resemblances.
Mathematical Elasticity: Theory of Shells
doi:10.1137/1.9781611976823
Philippe G. Ciarlet
Mathematical Elasticity: Theory of Shells
20220208T09:08:14Z
10.1137/1.9781611976823
https://epubs.siam.org/doi/book/10.1137/1.9781611976823?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Interpolatory Methods for Model Reduction
https://epubs.siam.org/doi/book/10.1137/1.9781611976083?mi=9qwetu&af=R&pubType=book&target=browse
Interpolatory Methods for Model Reduction. <br/> Dynamical systems are at the core of computational models for a wide range of complex phenomena and, as a consequence, the simulation of dynamical systems has become a fundamental tool in the modeling, prediction, and control of these phenomena. The insatiable need for better predictions and improved accuracy drives the evolution of computational models toward ever greater size and complexity, often linking together subsystems that may themselves represent diverse physics, diverse time scales, and diverse spatial scales. The implied demands on computational resources can easily exceed the limits of tractability,1 setting the stage for model reduction.
Interpolatory Methods for Model Reduction. <br/> Dynamical systems are at the core of computational models for a wide range of complex phenomena and, as a consequence, the simulation of dynamical systems has become a fundamental tool in the modeling, prediction, and control of these phenomena. The insatiable need for better predictions and improved accuracy drives the evolution of computational models toward ever greater size and complexity, often linking together subsystems that may themselves represent diverse physics, diverse time scales, and diverse spatial scales. The implied demands on computational resources can easily exceed the limits of tractability,1 setting the stage for model reduction.
Interpolatory Methods for Model Reduction
doi:10.1137/1.9781611976083
A. C. Antoulas
C. A. Beattie
S. Güğercin
Interpolatory Methods for Model Reduction
20200210T06:54:07Z
10.1137/1.9781611976083
https://epubs.siam.org/doi/book/10.1137/1.9781611976083?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Handbook of Writing for the Mathematical Sciences, Third Edition
https://epubs.siam.org/doi/book/10.1137/1.9781611976106?mi=9qwetu&af=R&pubType=book&target=browse
Handbook of Writing for the Mathematical Sciences, Third Edition. <br/> In this book I aim to describe most of what a scientist needs to know about mathematical writing. Although the focus in on mathematical writing, most of the book is applicable to scientific writing in general.
Handbook of Writing for the Mathematical Sciences, Third Edition. <br/> In this book I aim to describe most of what a scientist needs to know about mathematical writing. Although the focus in on mathematical writing, most of the book is applicable to scientific writing in general.
Handbook of Writing for the Mathematical Sciences, Third Edition
doi:10.1137/1.9781611976106
Nicholas J. Higham
Handbook of Writing for the Mathematical Sciences, Third Edition
20201019T07:31:22Z
10.1137/1.9781611976106
https://epubs.siam.org/doi/book/10.1137/1.9781611976106?mi=9qwetu&af=R&pubType=book&target=browse
© 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.

Solving Problems in Multiply Connected Domains
https://epubs.siam.org/doi/book/10.1137/1.9781611976151?mi=9qwetu&af=R&pubType=book&target=browse
Solving Problems in Multiply Connected Domains. <br/> Whenever two or more objects, or entities, be they bubbles, vortices, black holes, magnets, colloidal particles, microorganisms, swimming bacteria, Brownian random walkers, airfoils, turbine blades, electrified drops, magnetized particles, dislocations, cracks, or heterogeneities in an elastic solid, interact in some ambient medium, those objects or entities make “holes” in that medium. At least, that is how a mathematician would see it. Mathematicians call the holey regions containing a collection of interacting entities multiply connected.
Solving Problems in Multiply Connected Domains. <br/> Whenever two or more objects, or entities, be they bubbles, vortices, black holes, magnets, colloidal particles, microorganisms, swimming bacteria, Brownian random walkers, airfoils, turbine blades, electrified drops, magnetized particles, dislocations, cracks, or heterogeneities in an elastic solid, interact in some ambient medium, those objects or entities make “holes” in that medium. At least, that is how a mathematician would see it. Mathematicians call the holey regions containing a collection of interacting entities multiply connected.
Solving Problems in Multiply Connected Domains
doi:10.1137/1.9781611976151
Darren Crowdy
Solving Problems in Multiply Connected Domains
20200420T07:23:19Z
10.1137/1.9781611976151
https://epubs.siam.org/doi/book/10.1137/1.9781611976151?mi=9qwetu&af=R&pubType=book&target=browse
© 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.