Explorations In Numerical Analysis
Explorations In Numerical Analysis

Author: James V Lambers

Publisher: World Scientific

Published: 2018-09-17

Total Pages: 675

ISBN-13: 9813220031

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This textbook introduces advanced undergraduate and early-career graduate students to the field of numerical analysis. This field pertains to the design, analysis, and implementation of algorithms for the approximate solution of mathematical problems that arise in applications spanning science and engineering, and are not practical to solve using analytical techniques such as those taught in courses in calculus, linear algebra or differential equations. Topics covered include error analysis, computer arithmetic, solution of systems of linear equations, least squares problems, eigenvalue problems, polynomial interpolation and approximation, numerical differentiation and integration, nonlinear equations, optimization, ordinary differential equations, and partial differential equations. For each problem considered, the presentation includes the derivation of solution techniques, analysis of their efficiency, accuracy and robustness, and details of their implementation, illustrated through the MATLAB programming language. This text is suitable for a year-long sequence in numerical analysis, and can also be used for a one-semester course in numerical linear algebra.

Explorations In Numerical Analysis: Python Edition
Explorations In Numerical Analysis: Python Edition

Author: James V Lambers

Publisher: World Scientific

Published: 2021-01-14

Total Pages: 691

ISBN-13: 9811227950

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This textbook is intended to introduce advanced undergraduate and early-career graduate students to the field of numerical analysis. This field pertains to the design, analysis, and implementation of algorithms for the approximate solution of mathematical problems that arise in applications spanning science and engineering, and are not practical to solve using analytical techniques such as those taught in courses in calculus, linear algebra or differential equations.Topics covered include computer arithmetic, error analysis, solution of systems of linear equations, least squares problems, eigenvalue problems, nonlinear equations, optimization, polynomial interpolation and approximation, numerical differentiation and integration, ordinary differential equations, and partial differential equations. For each problem considered, the presentation includes the derivation of solution techniques, analysis of their efficiency, accuracy and robustness, and details of their implementation, illustrated through the Python programming language.This text is suitable for a year-long sequence in numerical analysis, and can also be used for a one-semester course in numerical linear algebra.

Explorations In Numerical Analysis: Python Edition
Explorations In Numerical Analysis: Python Edition

Author: JAMES V. SUMNER LAMBERS (AMBER C. MONTIFORTE, VIVIAN A.)

Publisher: World Scientific Publishing Company

Published: 2021-03

Total Pages: 680

ISBN-13: 9789811229343

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This textbook is intended to introduce advanced undergraduate and early-career graduate students to the field of numerical analysis. This field pertains to the design, analysis, and implementation of algorithms for the approximate solution of mathematical problems that arise in applications spanning science and engineering, and are not practical to solve using analytical techniques such as those taught in courses in calculus, linear algebra or differential equations. Topics covered include computer arithmetic, error analysis, solution of systems of linear equations, least squares problems, eigenvalue problems, nonlinear equations, optimization, polynomial interpolation and approximation, numerical differentiation and integration, ordinary differential equations, and partial differential equations. For each problem considered, the presentation includes the derivation of solution techniques, analysis of their efficiency, accuracy and robustness, and details of their implementation, illustrated through the Python programming language. This text is suitable for a year-long sequence in numerical analysis, and can also be used for a one-semester course in numerical linear algebra.

"Numerical Methods using Python (For scientists and Engineers)"

Author: Pankaj Dumka

Publisher: Blue Rose Publishers

Published: 2022-11-21

Total Pages: 128

ISBN-13:

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The book is specifically intended for scientists, engineers, and engineering students who have taken a course on numeric methods and wish to comprehend and learn the subject through programming. The book's chapters are written methodically (step-by-step) so that programming becomes simple. More emphasis is placed on computationally modelling the methodologies and discussing the numerical method. Python is chosen as the programming language because it is simple to comprehend and use compared to other programming languages. The book allows readers to use and experiment with the approaches it describes. With very few adjustments, many of the programmes in the book can be utilised for applications in science and engineering.

Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence

Author: Stephen Lynch

Publisher: CRC Press

Published: 2023-06-15

Total Pages: 334

ISBN-13: 100088967X

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Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI). This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling. Features: No prior experience of programming is required. Online GitHub repository available with codes for readers to practice. Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing. Full solutions to exercises are available as Jupyter notebooks on the Web. Support Material GitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/ Solutions to All Exercises: Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/Solutions_Section_1.html Section 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/Solutions_Section_2.html Section 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/Solutions_Section_3.html

Explorations in Monte Carlo Methods
Explorations in Monte Carlo Methods

Author: Ronald W. Shonkwiler

Publisher: Springer Science & Business Media

Published: 2009-08-11

Total Pages: 249

ISBN-13: 0387878378

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Monte Carlo methods are among the most used and useful computational tools available today, providing efficient and practical algorithims to solve a wide range of scientific and engineering problems. Applications covered in this book include optimization, finance, statistical mechanics, birth and death processes, and gambling systems. Explorations in Monte Carlo Methods provides a hands-on approach to learning this subject. Each new idea is carefully motivated by a realistic problem, thus leading from questions to theory via examples and numerical simulations. Programming exercises are integrated throughout the text as the primary vehicle for learning the material. Each chapter ends with a large collection of problems illustrating and directing the material. This book is suitable as a textbook for students of engineering and the sciences, as well as mathematics.

Numerical Algorithms
Numerical Algorithms

Author: Justin Solomon

Publisher: CRC Press

Published: 2015-06-24

Total Pages: 400

ISBN-13: 1482251892

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Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig

Finite Difference Computing with Exponential Decay Models
Finite Difference Computing with Exponential Decay Models

Author: Hans Petter Langtangen

Publisher: Springer

Published: 2016-06-10

Total Pages: 210

ISBN-13: 3319294393

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This text provides a very simple, initial introduction to the complete scientific computing pipeline: models, discretization, algorithms, programming, verification, and visualization. The pedagogical strategy is to use one case study – an ordinary differential equation describing exponential decay processes – to illustrate fundamental concepts in mathematics and computer science. The book is easy to read and only requires a command of one-variable calculus and some very basic knowledge about computer programming. Contrary to similar texts on numerical methods and programming, this text has a much stronger focus on implementation and teaches testing and software engineering in particular.

Programming Language Explorations
Programming Language Explorations

Author: Ray Toal

Publisher: CRC Press

Published: 2017-08-09

Total Pages: 379

ISBN-13: 1315314312

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Programming Language Explorations is a tour of several modern programming languages in use today. The book teaches fundamental language concepts using a language-by-language approach. As each language is presented, the authors introduce new concepts as they appear, and revisit familiar ones, comparing their implementation with those from languages seen in prior chapters. The goal is to present and explain common theoretical concepts of language design and usage, illustrated in the context of practical language overviews. Twelve languages have been carefully chosen to illustrate a wide range of programming styles and paradigms. The book introduces each language with a common trio of example programs, and continues with a brief tour of its basic elements, type system, functional forms, scoping rules, concurrency patterns, and sometimes, metaprogramming facilities. Each language chapter ends with a summary, pointers to open source projects, references to materials for further study, and a collection of exercises, designed as further explorations. Following the twelve featured language chapters, the authors provide a brief tour of over two dozen additional languages, and a summary chapter bringing together many of the questions explored throughout the text. Targeted to both professionals and advanced college undergraduates looking to expand the range of languages and programming patterns they can apply in their work and studies, the book pays attention to modern programming practice, covers cutting-edge languages and patterns, and provides many runnable examples, all of which can be found in an online GitHub repository. The exploration style places this book between a tutorial and a reference, with a focus on the concepts and practices underlying programming language design and usage. Instructors looking for material to supplement a programming languages or software engineering course may find the approach unconventional, but hopefully, a lot more fun.

Computer-Aided Numerical Methods in Psychology
Computer-Aided Numerical Methods in Psychology

Author: PressGrup Academician Team

Publisher: Prof. Dr. Bilal Semih Bozdemir

Published:

Total Pages: 492

ISBN-13:

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Psychology: Computer-Aided Numerical Methods Introduction to Numerical Methods in Psychology Advantages of Computer-Aided Numerical Analysis Data Collection and Preprocessing Linear Regression and Correlation Analysis Logistic Regression and Classification Principal Component Analysis (PCA) Cluster Analysis Time Series Analysis Bayesian Methods and Inference Monte Carlo Simulation Techniques Optimization Algorithms in Psychological Research Visualization and Interpretation of Results Practical Applications and Case Studies