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Numerical methods are techniques to approximate mathematical procedures (e.g., integrals). Approximations are needed because we either cannot solve the procedure analytically (e.g., the standard normal cumulative distribution function) or because the analytical method is intractable (e.g., solving a set of a thousand simultaneous linear equations for a thousand unknowns). By end of this course, participants will be able to apply the numerical methods for the following mathematical procedures and topics: differentiation, nonlinear equations, and simultaneous linear equations, interpolation, regression, integration, and ordinary differential equations. Additionally, they will be able to calculate errors and implement their relationship to the accuracy of the numerical solutions. To be prepared for this course, students should have a passing grade in introductory physics, integral calculus, differential calculus, and ordinary differential equations.
 Course related:
 BSE3302 Computer Methods in Building Services Engineering
 Subjects:
 Mathematics and Statistics
 Keywords:
 Numerical analysis Numerical calculations
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 Others

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Nicholas John Higham FRS is a British numerical analyst. He is Royal Society Research Professor and Richardson Professor of Applied Mathematics in the School of Mathematics at the University of Manchester. In this blog, it covers the popular topic, such as: (1) Top 5 Beamer Tips (2) The Nearest Correlation Matrix (3) The Top 10 Algorithms in Applied Mathematics (4) A Black Background for More Restful PDF viewing (5) Typesetting Mathematics According to the ISO Standard (6) Fourth Edition (2013) of Golub and Van Loan’s Matrix Computations (7) The Rise of Mixed Precision Arithmetic (8) Second Edition (2013) of Matrix Analysis by Horn and Johnson (9) Half Precision Arithmetic: fp16 Versus bfloat16 (10) Managing BibTeX Files with Emacs (11) Five Examples of Proofreading (12) Implicit Expansion: A Powerful New Feature of MATLAB R2016b (13) Dot Grid Paper for Writing Mathematics (14) Programming Languages: An Applied Mathematics View (15) Three BibTeX Tips (16) Better LaTeX Tables with Booktabs (17) The Princeton Companion to Applied Mathematics (18) Numerical Methods That (Usually) Work (19) What’s New in MATLAB R2017a? (20) What Is Numerical Stability?
 Course related:
 AMA615 Nonlinear Optimization Methods and AMA611 Applied Analysis
 Subjects:
 Mathematics and Statistics
 Keywords:
 Computer programming Numerical analysis
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 Others

ebook
The book is based on “First semester in Numerical Analysis with Julia”, written by Giray Ökten. The contents of the original book are retained, while all the algorithms are implemented in Python (Version 3.8.0). Python is an open source (under OSI), interpreted, generalpurpose programming language that has a large number of users around the world. Python is ranked the third in August 2020 by the TIOBE programming community index, a measure of popularity of programming languages, and is the topranked interpreted language. We hope this book will better serve readers who are interested in a first course in Numerical Analysis, but are more familiar with Python for the implementation of the algorithms. The first chapter of the book has a selfcontained tutorial for Python, including how to set up the computer environment. Anaconda, the opensource individual edition, is recommended for an easy installation of Python and effortless management of Python packages, and the Jupyter environment, a webbased interactive development environment for Python as well as many other programming languages, was used throughout the book and is recommended to the readers for easy code development, graph visualization and reproducibility.
 Subjects:
 Computing
 Keywords:
 Numerical analysis Computer programming Programming languages (Electronic computers) Textbooks Python (Computer program language)
 Resource Type:
 ebook

ebook
This text is intended to support courses that bridge the divide between mathematics typically encountered in U.S. high school curricula and the practical problems that natural resource students might engage with in their disciplinary coursework and professional internships.
 Subjects:
 Mathematics and Statistics
 Keywords:
 Problem solving Applied mathematics Textbooks Numerical analysis
 Resource Type:
 ebook