This textbook gives a unified treatment of the solution of various linear equations that arise in science and engineering with examples. It is based on a course taught by the first author for over thirty years. Some unique features include:
Use of symbolic software for illustrating and enhancing the impact of physical parameter changes on solutions.
Multi-scale analysis of engineering problems with physical interpretation of time and length scales in terms of eigenvalues and eigenvectors/eigenfunctions.
Discussion of compartment models for various finite dimensional problems.
Evaluation and illustration of functions of matrices (and use of symbolic manipulation) to solve multi-component diffusion-convection-reaction problems.
Illustration of the techniques and interpretation of solutions to several classical engineering problems.
Emphasis on the connection between discrete (matrix algebra) and continuum.
Physical interpretation of adjoint operator and adjoint systems.
Use of complex analysis and algebra in the solution of practical engineering problems.
Автор: Galina Filipuk, Andrzej Kozlowski Название: Analysis with Mathematica®: Volume 1: Single Variable Calculus ISBN: 3110590131 ISBN-13(EAN): 9783110590135 Издательство: Walter de Gruyter Цена: 74320.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A computer algebra system such as Mathematica is able to do much more than just numerics: This text shows how to tackle real mathematical problems from basic analysis. The reader learns how Mathematica represents domains, qualifiers and limits to implement actual proofs – a requirement to unlock the huge potential of Mathematica for a variety of applications.
Автор: Gregory Название: Bayesian Logical Data Analysis for the Physical Sciences ISBN: 0521150124 ISBN-13(EAN): 9780521150125 Издательство: Cambridge Academ Рейтинг: Цена: 69690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. Background material is provided in appendices and supporting Mathematica (R) notebooks are available.
Автор: Magrab Название: An Engineer`s Guide to Mathematica® ISBN: 1118821262 ISBN-13(EAN): 9781118821268 Издательство: Wiley Рейтинг: Цена: 84430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Free Mathematica 10 Update Included! Now available from www. wiley.
Автор: Hal R. Varian Название: Computational Economics and Finance ISBN: 1461275105 ISBN-13(EAN): 9781461275107 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book/software package divulges the combined knowledge of a whole international community of Mathematica users - from the fields of economics, finance, investments, quantitative business and operations research.
Автор: Bruce F. Torrence, Eve A. Torrence Название: The Student`s Introduction to Mathematica and the Wolfram Language ISBN: 110840636X ISBN-13(EAN): 9781108406369 Издательство: Cambridge Academ Рейтинг: Цена: 51750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces Mathematica (R) and the Wolfram Language (TM) in the context of the standard university mathematics curriculum. It equips current and former students to harness these tools to explore ideas from pre-calculus, calculus, and linear algebra. Additional chapters on programming and 3D printing provide outlets for further exploration.
Автор: Shardt Yuri a. W. Название: Statistics for Chemical and Process Engineers: A Modern Approach ISBN: 3030831892 ISBN-13(EAN): 9783030831899 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A coherent, concise, and comprehensive course in the statistics needed for a modern career in chemical engineering covers all of the concepts required for the American Fundamentals of Engineering Examination. Statistics for Chemical and Process Engineers (second edition) shows the reader how to develop and test models, design experiments and analyze data in ways easily applicable through readily available software tools like MS Excel® and MATLAB® and is updated for the most recent versions of both. Generalized methods that can be applied irrespective of the tool at hand are a key feature of the text, and it now contains an introduction to the use of state-space methods. The reader is given a detailed framework for statistical procedures covering: * data visualization; * probability; * linear and nonlinear regression; * experimental design (including factorial and fractional factorial designs); and * dynamic process identification. Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB are also available for download. With its integrative approach to system identification, regression, and statistical theory, this book provides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries, and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.
Автор: Christoph Borgers Название: Introduction to Numerical Linear Algebra ISBN: 161197691X ISBN-13(EAN): 9781611976915 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66050.00 T Наличие на складе: Поставка под заказ. Описание: Fit for students just starting to build a background in mathematics, this textbook provides an introduction to numerical methods for linear algebra problems.Introduction to Numerical Linear Algebrais ideal for a flipped classroom, as it provides detailed explanations that allow students to read on their own and instructors to go beyond lecturing, assumes that the reader has taken a course on linear algebra, but reviews background as needed, andcovers several topics not commonly addressed in related introductory books, including diffusion, a toy model of computed tomography, global positioning systems, the use of eigenvalues in analyzing stability of equilibria, a detailed derivation and careful motivation of the QR method for eigenvalues starting from power iteration, a discussion of the use of the SVD for assigning grades, and multigrid methods.This textbook is appropriate for undergraduate and beginning graduate students in mathematics and related fields. It can be used in the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory
Автор: William W. Hager Название: Applied Numerical Linear Algebra ISBN: 1611976855 ISBN-13(EAN): 9781611976854 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 74410.00 T Наличие на складе: Поставка под заказ. Описание: Applied Numerical Linear Algebra introduces students to numerical issues that arise in linear algebra and its applications. A wide range of techniques are touched on, including direct to iterative methods, orthogonal factorizations, least squares, eigenproblems, and nonlinear equations.Inside Applied Numerical Linear Algebra, readers will find:Clear and detailed explanations on a wide range of topics from condition numbers to the singular value decomposition.Material on nonlinear systems as well as linear systems.Frequent illustrations using discretizations of boundary-value problems or demonstrating other concepts.Exercises with detailed solutions at the end of the book.Supplemental material available at https://bookstore.siam.org/cl87/bonus.This textbook is appropriate for junior and senior undergraduate students and beginning graduate students in the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory.
Автор: Wendland Holger Название: Numerical Linear Algebra ISBN: 131660117X ISBN-13(EAN): 9781316601174 Издательство: Cambridge Academ Рейтинг: Цена: 40120.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This self-contained introduction to numerical linear algebra provides a comprehensive, yet concise, overview of the subject. This book will be of particular use to applied mathematicians, engineers, computer scientists, and to all those interested in efficiently solving linear problems.
Автор: Ciaramella, Gabriele Gander, Martin J. Название: Iterative methods and preconditioners for systems of linear equations ISBN: 1611976898 ISBN-13(EAN): 9781611976892 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66050.00 T Наличие на складе: Поставка под заказ. Описание: Iterative methods use successive approximations to obtain more accurate solutions. Iterative Methods and Preconditioners for Systems of Linear Equationspresents 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.
Автор: Cui, Ying Pang, Jong-shi Название: Modern nonconvex nondifferentiable optimization ISBN: 1611976731 ISBN-13(EAN): 9781611976731 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 99490.00 T Наличие на складе: Поставка под заказ. Описание: Starting with the fundamentals of classical smooth optimization and building on established convex programming techniques, this research monograph presents a foundation and methodology for modern nonconvex nondifferentiable optimization. It provides readers with theory, methods, and applications of nonconvex and nondifferentiable optimization in statistical estimation, operations research, machine learning, and decision making. A comprehensive and rigorous treatment of this emergent mathematical topic is urgently needed in today's complex world of big data and machine learning. This book takes a thorough approach to the subject and includes examples and exercises to enrich the main themes, making it suitable for classroom instruction. Modern Nonconvex Nondifferentiable Optimization is intended for applied and computational mathematicians, optimizers, operations researchers, statisticians, computer scientists, engineers, economists, and machine learners. It could be used in advanced courses on optimization/operations research and nonconvex and nonsmooth optimization.
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