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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
 Resource Type:
 Others

ebook
This textbook was born of a desire to contribute a viable, free, introductory Numerical Analysis textbook for instructors and students of mathematics. The ultimate goal of Tea Time Numerical Analysis is to be a complete, onesemester, singlepdf, downloadable textbook designed for mathematics classes. Now includes differential equations. Over 350 pages Over 1000 lines of code Over 200 figures Open source
 Subjects:
 Mathematics and Statistics
 Keywords:
 Numerical analysis  Data processing Textbooks
 Resource Type:
 ebook

ebook
First Semester in Numerical Analysis with Julia presents the theory and methods, together with the implementation of the algorithms using the Julia programming language (version 1.1.0). The book covers computer arithmetic, rootfinding, numerical quadrature and differentiation, and approximation theory. The reader is expected to have studied calculus and linear algebra. Some familiarity with a programming language is beneficial, but not required. The programming language Julia will be introduced in the book. The simplicity of Julia allows bypassing the pseudocode and writing a computer code directly after the description of a method while minimizing the distraction the presentation of a computer code might cause to the flow of the main narrative.
 Subjects:
 Mathematics and Statistics
 Keywords:
 Numerical analysis  Data processing Mathematics Julia (Computer programming language) Textbooks
 Resource Type:
 ebook