Iterative methods use successive approximations to obtain more accurate solutions. Iterative Methods and Preconditioners for Systems of Linear Equations presents historical background, derives complete convergence estimates for all methods, illustrates and provides Matlab codes for all methods, and studies and tests all preconditioners first as stationary iterative solvers. This textbook is appropriate for undergraduate and graduate students in need of an overview or of deeper knowledge about iterative methods. It can be used in courses on Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory. Scientists and engineers interested in new topics and applications will also find the text useful.

Symbolic rewriting techniques are methods for deriving consequences from systems of equations, and are of great use when investigating the structure of the solutions. Such techniques appear in many important areas of research within computer algebra: a the Knuth-Bendix completion for groups, monoids and general term-rewriting systems, a the Buchberger algorithm for GrAbner bases, a the Ritt-Wu characteristic set method for ordinary differential equations, and a the Riquier-Janet method for partial differential equations. This volume contains invited and contributed papers to the Symbolic Rewriting Techniques workshop, which was held at the Centro Stefano Franscini in Ascona, Switzerland, from April 30 to May 4, 1995. That workshop brought together 40 researchers from various areas of rewriting techniques, the main goal being the investigation of common threads and methods. Following the workshops, each contribution was formally refereed and 14 papers were selected for publication.

Matrix algorithms are at the core of scientific computing and are indispensable tools in most applications in engineering. This book offers a comprehensive and up-to-date treatment of modern methods in matrix computation. It uses a unified approach to direct and iterative methods for linear systems, least squares and eigenvalue problems. A thorough analysis of the stability, accuracy and complexity of the treated methods is given.

"Numerical Methods in Matrix Computations" is suitable for use in courses on scientific computing and applied technical areas at advanced undergraduate and graduate level. A large bibliography is provided, which includes both historical and review papers as well as recent research papers. This makes the book useful also as a reference and guide to further study and research work.

This book is based upon the collaborative efforts of the Ontogenetics Process Group (OPG) - an interdisciplinary, multi-institutional, multi-national research group that began meeting in 2017 to explore new and innovative ways of thinking the problem of complexity in living, physical, and social systems outside the algorithmic models that have dominated paradigms of complexity to date. For all the descriptive and predictive power that the complexity sciences offer (the ability to compute feedback systems, recursive networks, emergent dynamics, etc.), they also presume that the living world in all of its modalities (biological, semiotic, economic, affective, social) can be reduced to finite schema of description that delimits in advance all possible outcomes. What is proposed in this volume are conceptual architectures for the living that are not only irreducible to physico-mathematical frames of reference, but that are also as vital as the phenomena they wish to express. In short: life is more complex than complexity. What emerges from this engagement is not the ascendance of a new transcendental principle (or, what amounts to the same thing, a foundational bedrock) derived from the physico-mathematical sciences, but just the opposite: a domain in which the ontological and the epistemological domains enter a zone of strange (and unavoidable) entanglement. The chapters in this book were originally published as a special issue of Angelaki.

Information is a basic structure of the world, while computation is a process of the dynamic change of information. This book provides a cutting-edge view of world's leading authorities in fields where information and computation play a central role. It sketches the contours of the future landscape for the development of our understanding of information and computation, their mutual relationship and the role in cognition, informatics, biology, artificial intelligence, and information technology.

This book is an utterly enjoyable and engaging read which gives readers an opportunity to understand and relate phenomena seemingly unrelated in a completely new light especially the connections between information, computation, cognition and life.

The book is a collection of original papers, research and surveys, dedicated to the memory of the Romanian mathematician Solomon Marcus (1925-2016). Marcus published many papers and books in mathematical analysis, theoretical computer science, mathematical linguistics, poetics, theory of literature, semiotics, and several other fields less strongly connected to mathematics, like cultural anthropology, biology, history and philosophy of science, education. He exemplified an unimaginable richness of ideas.This volume intends to emphasize the mathematical fields in which Solomon Marcus worked, and demonstrate - as he also did - the interconnection between them. The authors who contribute to this volume are well-known experts in their fields. Most of them knew Solomon Marcus well, some even owed him for his decisive impulses for their careers and general development. With articles in so diverse areas, the volume will attract readers who would like to diversify their own knowledge or find unexpected connections with other topics.

This contributed volume offers a collection of papers presented at the 2018 Network Games, Control, and Optimization conference (NETGCOOP), held at the New York University Tandon School of Engineering in New York City, November 14-16, 2018. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other related fields.

This book compares the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies, and evolutionary programming - computational methods at the border between computer science and evolutionary biology. The algorithms are explained within a common formal framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms and uses a meta-evolutionary approach to confirm some of the theoretical results.

1 Mathematical Preliminaries.- 1.1 The Pythagorean Theorem.- 1.2 Vectors.- 1.3 Subspaces and Linear Independence.- 1.4 Vector Space Bases.- 1.5 Euclidean Length.- 1.6 The Euclidean Inner Product.- 1.7 Projection onto a Line.- 1.8 Planes in-Space.- 1.9 Coordinate System Orientation.- 1.10 The Cross Product.- 2 Curves.- 2.1 The Tangent Curve.- 2.2 Curve Parameterization.- 2.3 The Normal Curve.- 2.4 Envelope Curves.- 2.5 Arc Length Parameterization.- 2.6 Curvature.- 2.7 The Frenet Equations.- 2.8 Involutes and Evolutes.- 2.9 Helices.- 2.10 Signed Curvature.- 2.11 Inflection Points.- 3 Surfaces.- 3.1 The Gradient of a Function.- 3.2 The Tangent Space and Normal Vector.- 3.3 Derivatives.- 4 Function and Space Curve Interpolation.- 5 2D-Function Interpolation.- 5.1 Lagrange Interpolating Polynomials.- 5.2 Whittaker's Interpolation Formula.- 5.3 Cubic Splines for 2D-Function Interpolation.- 5.4 Estimating Slopes.- 5.5 Monotone 2D Cubic Spline Functions.- 5.6 Error in 2D Cubic Spline Interpolation Functions.- 6 ?-Spline Curves With Range Dimension d.- 7 Cubic Polynomial Space Curve Splines.- 7.1 Choosing the Segment Parameter Limits.- 7.2 Estimating Tangent Vectors.- 7.3 Bezier Polynomials.- 8 Double Tangent Cubic Splines.- 8.1 Kochanek-Bartels Tangents.- 8.2 Fletcher-McAllister Tangent Magnitudes.- 9 Global Cubic Space Curve Splines.- 9.1 Second Derivatives of Global Cubic Splines.- 9.2 Third Derivatives of Global Cubic Splines.- 9.3 A Variational Characterization of Natural Splines.- 9.4 Weighted v-Splines.- 10 Smoothing Splines.- 10.1 Computing an Optimal Smoothing Spline.- 10.2 Computing the Smoothing Parameter.- 10.3 Best Fit Smoothing Cubic Splines.- 10.4 Monotone Smoothing Splines.- 11 Geometrically Continuous Cubic Splines.- 11.1 Beta Splines.- 12 Quadratic Space Curve Based Cubic Splines.- 13 Cubic Spline Vector Space Basis Functions.- 13.1 Bases for C1 and C2 Space Curve Cubic Splines.- 13.2 Cardinal Bases for Cubic Spline Vector Spaces.- 13.3 The B-Spline Basis for Global Cubic Splines.- 14 Rational Cubic Splines.- 15 Two Spline Programs.- 15.1 Interpolating Cubic Splines Program.- 15.2 Optimal Smoothing Spline Program.- 16 Tensor Product Surface Splines.- 16.1 Bicubic Tensor Product Surface Patch Splines.- 16.2 A Generalized Tensor Product Patch Spline.- 16.3 Regular Grid Multi-Patch Surface Interpolation.- 16.4 Estimating Tangent and Twist Vectors.- 16.5 Tensor Product Cardinal Basis Representation.- 16.6 Bicubic Splines with Variable Parameter Limits.- 16.7 Triangular Patches.- 16.8 Parametric Grids.- 16.9 3D-Function Interpolation.- 17 Boundary Curve Based Surface Splines.- 17.1 Boundary Curve Based Bilinear Interpolation.- 17.2 Boundary Curve Based Bicubic Interpolation.- 17.3 General Boundary Curve Based Spline Interpolation.- 18 Physical Splines.- 18.1 Computing a Space Curve Physical Spline Segment.- 18.2 Computing a 2D Physical Spline Segment.- References.

Algebraic logic is a subject in the interface between logic, algebra and geometry, it has strong connections with category theory and combinatorics. Tarski s quest for finding structure in logic leads to cylindric-like algebras as studied in this book, they are among the main players in Tarskian algebraic logic. Cylindric algebra theory can be viewed in many ways: as an algebraic form of definability theory, as a study of higher-dimensional relations, as an enrichment of Boolean Algebra theory, or, as logic in geometric form ( cylindric in the name refers to geometric aspects). Cylindric-like algebras have a wide range of applications, in, e.g., natural language theory, data-base theory, stochastics, and even in relativity theory. The present volume, consisting of 18 survey papers, intends to give an overview of the main achievements and new research directions in the past 30 years, since the publication of the Henkin-Monk-Tarski monographs. It is dedicated to the memory of Leon Henkin. "

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. "Try This!" exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.

Computability has played a crucial role in mathematics and computer science, leading to the discovery, understanding and classification of decidable/undecidable problems, paving the way for the modern computer era, and affecting deeply our view of the world. Recent new paradigms of computation, based on biological and physical models, address in a radically new way questions of efficiency and challenge assumptions about the so-called Turing barrier.

This volume addresses various aspects of the ways computability and theoretical computer science enable scientists and philosophers to deal with mathematical and real-world issues, covering problems related to logic, mathematics, physical processes, real computation and learning theory. At the same time it will focus on different ways in which computability emerges from the real world, and how this affects our way of thinking about everyday computational issues.

The list of contributors includes: S Abramsky, P Adriaans, M Agrawal, M Arslanov, G Ausiello, J Diaz, Y Ershov, G Longo, W Maass, I Nemeti, A Nerode, D Normann, G Odifreddi, M Rathjen, G Rozenberg, M Vardi, and P Welch.

Nonlinear Partial Differential Equations (PDEs) have become increasingly important in the description of physical phenomena. Unlike Ordinary Differential Equations, PDEs can be used to effectively model multidimensional systems. The methods put forward in Discrete Variational Derivative Method concentrate on a new class of "structure-preserving numerical equations" which improves the qualitative behaviour of the PDE solutions and allows for stable computing. The authors have also taken care to present their methods in an accessible manner, which means that the book will be useful to engineers and physicists with a basic knowledge of numerical analysis. Topics discussed include: * "Conservative" equations such as the Korteweg-de Vries equation (shallow water waves) and the nonlinear Schr dinger equation (optical waves) * "Dissipative" equations such as the Cahn-Hilliard equation (some phase separation phenomena) and the Newell-Whitehead equation (two-dimensional B nard convection flow) * Design of spatially and temporally high-order schemas * Design of linearly-implicit schemas * Solving systems of nonlinear equations using numerical Newton method libraries

This book introduces linear transformation and its key results, which have applications in engineering, physics, and various branches of mathematics. Linear transformation is a difficult subject for students. This concise text provides an in-depth overview of linear trans-formation. It provides multiple-choice questions, covers enough examples for the reader to gain a clear understanding, and includes exact methods with specific shortcuts to reach solutions for particular problems. Research scholars and students working in the fields of engineering, physics, and different branches of mathematics need to learn the concepts of linear transformation to solve their problems. This book will serve their need instead of having to use the more complex texts that contain more concepts then needed. The chapters mainly discuss the definition of linear transformation, properties of linear transformation, linear operators, composition of two or more linear transformations, kernels and range of linear transformation, inverse transformation, one-to-one and onto transformation, isomorphism, matrix linear transformation, and similarity of two matrices.

Until recently, all of the interactions between objects in virtual 3D worlds have been based on calculations performed using linear algebra. Linear algebra relies heavily on coordinates, however, which can make many geometric programming tasks very specific and complex-often a lot of effort is required to bring about even modest performance enhancements. Although linear algebra is an efficient way to specify low-level computations, it is not a suitable high-level language for geometric programming. "Geometric Algebra for Computer Science" presents a compelling alternative to the limitations of linear algebra. Geometric algebra, or GA, is a compact, time-effective, and performance-enhancing way to represent the geometry of 3D objects in computer programs. In this book you will find an introduction to GA that will give you a strong grasp of its relationship to linear algebra and its significance for your work. You will learn how to use GA to represent objects and perform geometric operations on them. And you will begin mastering proven techniques for making GA an integral part of your applications in a way that simplifies your code without slowing it down.
Explains GA as a natural extension of linear algebra and conveys its significance for 3D programming of geometry in graphics, vision, and robotics. Systematically explores the concepts and techniques that are key to representing elementary objects and geometric operators using GA. Covers in detail the conformal model, a convenient way to implement 3D geometry using a 5D representation space. Presents effective approaches to making GA an integral part of your programming. Includes numerous drills and programming exercises helpful for both students and practitioners. Companion web site includes links to GAViewer, a program that will allow you to interact with many of the 3D figures in the book, and Gaigen 2, the platform for the instructive programming exercises that conclude each chapter.

Mathematica, Maple, and similar software packages provide programs that carry out sophisticated mathematical operations. Applying the ideas introduced in Computer Algebra and Symbolic Computation: Elementary Algorithms, this book explores the application of algorithms to such methods as automatic simplification, polynomial decomposition, and polynomial factorization. This book includes complexity analysis of algorithms and other recent developments. It is well-suited for self-study and can be used as the basis for a graduate course. Maintaining the style set by Elementary Algorithms, the author explains mathematical methods as needed while introducing advanced methods to treat complex operations.

C++ is capable of tackling a whole range of programming tasks. The purpose of this book is to give breadth and depth to C++ programmers’ existing experience of the language by representing a large number of algorithms, most of them implemented as ready-to-run (and standalone) programs. The programs are as readable as possible without sacrificing too great a degree of efficiency, generality, portability and robustness. Both the classes and programs are designed to demonstrate major programming principles. There is coverage of two key language features - templates and exception handling - apart from which the reader is assumed to have working knowledge of C++. Besides traditional subjects, such as quicksort and binary trees, this book also covers some less well-known topics, including multi-precision arithmetic, route planning and external sorting. Demonstration programs for these and many other exciting applications are based on C++ classes which you can also use in programs of your own.

"The NCL Natural Constraint Language"" "presents the NCL language which is a description language in conventional mathematical logic for modeling and solving constraint satisfaction problems. NCL differs from other declarative languages: It models problems naturally in a simplified form of first-order logic with quantifiers, Boolean logic, numeric constraints, set operations and logical functions; it solves problems by mixed set programming over the mixed domain of real numbers, integers, Booleans, dates/times, references, and in particular sets. The book uses plenty of examples and tutorials to illustrate NCL and its applications. It is intended for researchers and developers in the fields of logic programming, constraint programming, optimization, modeling, operations research and artificial intelligence, who will learn from a new programming language and theoretical foundations for industrial applications.

Dr. Jianyang Zhou is the inventor of NCL and has worked for its industrialization for more than 10 years.

This two-volume set on Mathematical Principles of the Internet provides a comprehensive overview of the mathematical principles of Internet engineering. The books do not aim to provide all of the mathematical foundations upon which the Internet is based. Instead, they cover a partial panorama and the key principles. Volume 1 explores Internet engineering, while the supporting mathematics is covered in Volume 2. The chapters on mathematics complement those on the engineering episodes, and an effort has been made to make this work succinct, yet self-contained. Elements of information theory, algebraic coding theory, cryptography, Internet traffic, dynamics and control of Internet congestion, and queueing theory are discussed. In addition, stochastic networks, graph-theoretic algorithms, application of game theory to the Internet, Internet economics, data mining and knowledge discovery, and quantum computation, communication, and cryptography are also discussed. In order to study the structure and function of the Internet, only a basic knowledge of number theory, abstract algebra, matrices and determinants, graph theory, geometry, analysis, optimization theory, probability theory, and stochastic processes, is required. These mathematical disciplines are defined and developed in the books to the extent that is needed to develop and justify their application to Internet engineering.

This two-volume set on Mathematical Principles of the Internet provides a comprehensive overview of the mathematical principles of Internet engineering. The books do not aim to provide all of the mathematical foundations upon which the Internet is based. Instead, they cover a partial panorama and the key principles. Volume 1 explores Internet engineering, while the supporting mathematics is covered in Volume 2. The chapters on mathematics complement those on the engineering episodes, and an effort has been made to make this work succinct, yet self-contained. Elements of information theory, algebraic coding theory, cryptography, Internet traffic, dynamics and control of Internet congestion, and queueing theory are discussed. In addition, stochastic networks, graph-theoretic algorithms, application of game theory to the Internet, Internet economics, data mining and knowledge discovery, and quantum computation, communication, and cryptography are also discussed. In order to study the structure and function of the Internet, only a basic knowledge of number theory, abstract algebra, matrices and determinants, graph theory, geometry, analysis, optimization theory, probability theory, and stochastic processes, is required. These mathematical disciplines are defined and developed in the books to the extent that is needed to develop and justify their application to Internet engineering.

Automata and Computability is a class-tested textbook which provides a comprehensive and accessible introduction to the theory of automata and computation. The author uses illustrations, engaging examples, and historical remarks to make the material interesting and relevant for students. It incorporates modern/handy ideas, such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus. The book also shows how to sculpt automata by making the regular language conversion pipeline available through a simple command interface. A Jupyter notebook will accompany the book to feature code, YouTube videos, and other supplements to assist instructors and students Features Uses illustrations, engaging examples, and historical remarks to make the material accessible Incorporates modern/handy ideas, such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus Shows how to "sculpt" automata by making the regular language conversion pipeline available through simple command interface Uses a mini functional programming (FP) notation consisting of lambdas, maps, filters, and set comprehension (supported in Python) to convey math through PL constructs that are succinct and resemble math Provides all concepts are encoded in a compact Functional Programming code that will tesselate with Latex markup and Jupyter widgets in a document that will accompany the books. Students can run code effortlessly. All the code can be accessed here.

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Praise for the First Edition
Statistical Design and Analysis of Experiments

"A very useful book for self study and reference."
–Journal of Quality Technology

"Very well written. It is concise and really packs a lot of material in a valuable reference book."
–Technometrics

"An informative and well-written book . . . presented in an easy-to-understand style with many illustrative numerical examples taken from engineering and scientific studies."
–Choice (American Library Association)

Practicing engineers and scientists often have a need to utilize statistical approaches to solving problems in an experimental setting. Yet many have little formal training in statistics. Statistical Design and Analysis of Experiments gives such readers a carefully selected, practical background in the statistical techniques that are most useful to experimenters and data analysts who collect, analyze, and interpret data.

The First Edition of this now-classic book garnered praise in the field. Now its authors update and revise their text, incorporating readers’ suggestions as well as a number of new developments. Statistical Design and Analysis of Experiments, Second Edition emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results, presenting statistics as an integral component of experimentation from the planning stage to the presentation of conclusions.

Giving an overview of the conceptual foundations of modern statistical practice, the revised text features discussions of:

• The distinctions between populations or processes and samples; parameters and statistics; and mathematical and statistical modeling
• The design and analysis of experiments with factorial structures, unbalanced experiments, crossed and nested factors, and random factor effects
• Confidence-interval and hypothesis-testing procedures for single-factor and multifactor experiments
• Quantitative predictors and factors, including linear regression modeling using least-squares estimators, with diagnostic techniques for assessing model assumptions

Ideal for both students and professionals, this focused and cogent reference has proven to be an excellent classroom textbook with numerous examples. It deserves a place among the tools of every engineer and scientist working in an experimental setting.

This established and authoritative text focuses on the design and analysis of nonlinear control systems. The author considers the latest research results and techniques in this updated and extended edition. Examples are given from mechanical, electrical and aerospace engineering. The approach consists of a rigorous mathematical formulation of control problems and respective methods of solution. The two appendices outline the most important concepts of differential geometry and present some specific findings not often found in other standard works. The book is, therefore, suitable both as a graduate and undergraduate text and as a source for reference.

This textbook covers the mathematical foundations of the analysis of algorithms. The gist of the book is how to argue, without the burden of excessive formalism, that a given algorithm does what it is supposed to do. The two key ideas of the proof of correctness, induction and invariance, are employed in the framework of pre/post-conditions and loop invariants.

The algorithms considered are the basic and traditional algorithms of computer science, such as Greedy, Dynamic and Divide & Conquer. In addition, two classes of algorithms that rarely make it into introductory textbooks are discussed. Randomized algorithms, which are now ubiquitous because of their applications to cryptography; and Online algorithms, which are essential in fields as diverse as operating systems (caching, in particular) and stock-market predictions.

This self-contained book is intended for undergraduate students in computer science and mathematics.