Society for Industrial and Applied Mathematics – förlag

Optimization problems involving stochastic models occur in most areas of science and engineering, particularly telecommunications, medicine, and finance. Their existence reveals a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. In this second edition, the authors introduce new material to reflect recent developments, including: analytical descriptions of the tangent and normal cones of chance constrained sets; analysis of optimality conditions for nonconvex problems; a discussion of the stochastic dual dynamic programming method; an extended discussion of law invariant coherent risk measures and their Kusuoka representations; and an in-depth analysis of dynamic risk measures and concepts of time consistency, including several new results. This book is intended for researchers working in optimization. It is also suitable for advanced graduate courses in this area.

Systems of linear equations are ubiquitous in numerical analysis and scientific computing. and iterative methods are indispensable for the numerical treatment of such systems. This book offers a rigorous introduction to fundamental iterative methods for systems of linear algebraic equations. The book distinguishes itself from other texts on the topic by providing a straightforward yet comprehensive analysis of the Krylov subspace methods, approaching the development and analysis of algorithms from various perspectives, and going beyond the standard description of iterative methods by connecting them in a natural way to the idea of preconditioning. The book supplements standard texts on numerical mathematics for first-year graduate and advanced undergraduate courses and is suitable for advanced graduate classes covering numerical linear algebra and Krylov subspace and multigrid iterative methods. It will be useful to researchers interested in numerical linear algebra and engineers who use iterative methods for solving large algebraic systems.

Variational analysis is the subject of this self-contained guide, which provides a detailed presentation of the most important tools in the field, as well as applications to geometry, mechanics, elasticity, and computer vision. This second edition introduces significant new material on several topics, including: quasi-open sets and quasi-continuity in the context of capacity theory and potential theory; mass transportation problems and the Kantorovich relaxed formulation of the Monge problem; and stochastic homogenization, with mathematical tools coming from ergodic theory. It also features an entirely new and comprehensive chapter devoted to gradient flows and the dynamical approach to equilibria, and extra examples in the areas of linearized elasticity systems, obstacles problems, convection-diffusion, semilinear equations, and the shape optimization procedure. The book is intended for PhD students, researchers, and practitioners who want to approach the field of variational analysis in a systematic way.

The first book in the area, this volume addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space, which has applications in computational geometry, data representations, and optimal design in statistics. The book covers the formulation of this and related problems, theoretical properties of their optimal solutions, and algorithms for their solution. While algorithms of this kind have been discovered and rediscovered over the past fifty years, their computational complexities and convergence rates have only recently been investigated. The optimization problems in the book have the entries of a symmetric matrix as their variables, so the author's treatment also gives an introduction to recent work in matrix optimization. This book will be of interest to graduate students and researchers in operations research, theoretical statistics, data mining, complexity theory, computational geometry, and computational science.

LaTeX is an extremely popular typesetting system which is widely used in the sciences. This short, well-written book covers the material essential for learning LaTeX, without any extraneous detail. Incisive examples teach readers in a powerful, yet abbreviated fashion and complete documents are included to illustrate the creation of articles, reports, presentations, and posters. Also included are troubleshooting tips, to help readers pinpoint an error, and advice about how to set up indexes and bibliographies. In this second edition, additional material has been included on the American Mathematical Society packages for typesetting additional mathematical symbols and multi-line displays; the BiBTeX program for creating bibliographies; the Beamer package for creating presentations; and the a0poster class for creating posters.

Inverse scattering theory is a major theme of applied mathematics, and has applications to such diverse areas as medical imaging, geophysical exploration, and nondestructive testing. The inverse scattering problem is nonlinear and ill-posed, thus presenting particular problems in the development of efficient inversion algorithms. Initial efforts to address these problems focused on nonlinear optimization methods, which required strong a priori information. This led to a qualitative approach to inverse scattering theory in which the amount of a priori information is drastically reduced, although at the expense of only obtaining limited information about the values of constitutive parameters. This book provides an introduction to this important new direction in inverse scattering theory. Beginning with a basic introduction to the theory, the authors proceed to more recent developments, including a detailed discussion of the transmission eigenvalue problem. Both the new generalized linear sampling method and well-known linear sampling and factorization methods are included.

Phylogenetics is a growing area of research. Phylogenies (phylogenetic trees and networks) allow biologists to study and graph evolutionary relationships between different species. These are also used to investigate other evolutionary processes, including how languages developed or how different strains of a virus are related to each other. This self-contained book addresses the underlying mathematical theory behind the reconstruction and analysis of phylogenies. The theory is grounded in classical concepts from discrete mathematics and probability theory as well as techniques from other branches of mathematics (algebra, topology, differential equations). The biological relevance of the results is highlighted throughout, and the author supplies both proofs of classical theorems and results not covered in existing books. Relevant mathematical results derived over the past twenty years are emphasised, and numerous exercises, examples and figures are provided. The book will be ideal for any applied mathematician, biomathematician, discrete mathematician, systematic biologist, or computer scientist specialising in algorithms or bioinformatics.

Data-driven dynamical systems is a burgeoning field, connecting how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is the first book to address the DMD algorithm and present a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development. By blending theoretical development, example codes, and applications, the theory and its many innovations and uses are showcased. The efficacy of the DMD algorithm is shown through the inclusion of example problems from engineering, physical sciences, and biological sciences, and the authors provide extensive MATLAB® code, data for intuitive examples of key methods, and graphical presentations. This book can therefore be used in courses that integrate data analysis with dynamical systems, and will be a useful resource for engineers and applied mathematicians.

To quote a review by W. Ledermann in the Physics Bulletin, 'By skilfully blending the points of view of pure and applied mathematics, of physics, and of numerical methods, Professor Lanczos has succeeded in writing a book on Fourier series which is highly readable and informative. The subject itself is used as a leitmotiv to illuminate and revise the fundamental concepts of analysis ranging from the notion of a function to the method of contour integration.' Originally published in 1966, this well-written and still-cited text covers Fourier analysis, a foundation of science and engineering. The author meticulously develops the topic in a friendly, conversational tone to clarify the material and engage the reader. 161 problems are integrated into the text to walk undergraduate and graduate students of engineering, physics, and mathematics down the simplest path to a solution.

Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasising 'why' and not just 'how'. Methods and diagnostics are emphasised, enabling readers to readily apply them to their own field of study. This comprehensive guide is accessible to non-experts and contains numerous examples and diverse applications from a broad range of domains, including geophysics, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning. Readers will also find included the latest methods for advanced data assimilation, combining variational and statistical approaches.

Fluids that mix at geophysical or microscales tend to form well-mixed areas and regions of coherent blobs. The Antarctic circumpolar vortex, which mostly retains its structure while moving unsteadily in the atmosphere, is an example. How do such structures exchange fluid with their surroundings? What is the impact on global mixing? What is the 'boundary' of the structure, and how does it move? Can these questions be answered from time-varying observational data? This book addresses these issues from the perspective of the differential equations that must be obeyed by fluid particles. In these terms, identification of the boundaries of coherent structures (i.e. 'flow barriers'), quantification of transport across them, control of the locations of these barriers, and optimization of transport across them are developed using a rigorous mathematical framework. The concepts are illustrated with an array of theoretical and applied examples that arise from oceanography and microfluidics.

Principles of Computerized Tomographic Imaging provides a comprehensive, tutorial-style introduction to the algorithms for reconstructing cross-sectional images from projection data and contains a complete overview of the engineering and signal processing algorithms necessary for tomographic imaging. In addition to the purely mathematical and algorithmic aspects of these algorithms, the book also discusses the artifacts caused by the nature of the various forms of energy sources that can be used for generating the projection data. Kak and Slaney go beyond theory, emphasizing real-world applications and detailing the steps necessary for building a tomographic system. Since the fundamental aspects of tomographic reconstruction algorithms have remained virtually the same since this book was originally published, it is just as useful today as it was in 1987.