Overview
- Written at an introductory level
- Using JULIA on a non-trivial application level
- Includes discussion of JULIA as an open-source alternative for MATLAB
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
This book provides an introduction to modern topics in scientific computing and machine learning, using JULIA to illustrate the efficient implementation of algorithms. In addition to covering fundamental topics, such as optimization and solving systems of equations, it adds to the usual canon of computational science by including more advanced topics of practical importance. In particular, there is a focus on partial differential equations and systems thereof, which form the basis of many engineering applications. Several chapters also include material on machine learning (artificial neural networks and Bayesian estimation).
JULIA is a relatively new programming language which has been developed with scientific and technical computing in mind. Its syntax is similar to other languages in this area, but it has been designed to embrace modern programming concepts. It is open source, and it comes with a compiler and an easy-to-use package system.
Aimed at students ofapplied mathematics, computer science, engineering and bioinformatics, the book assumes only a basic knowledge of linear algebra and programming.
Similar content being viewed by others
Keywords
Table of contents (14 chapters)
-
Front Matter
-
Algorithms for Differential Equations
-
Front Matter
-
-
Algorithms for Optimization
-
Front Matter
-
-
Algorithms for Machine Learning
-
Front Matter
-
-
Back Matter
Reviews
“The author’s writing style is clear and concise, making the book easy to follow and understand. The book also includes useful code snippets and diagrams that help illustrate the concepts and algorithms discussed. … the book is well-written and an excellent resource for all those interested in learning the Julia language along with its applications. The extensive discussion of algorithms covering a variety of topics makes it a beneficial book for students, teachers, and researchers alike.” (Syed Inayatullah, zbMATH 1512.90003, 2023)
Authors and Affiliations
About the author
Clemens Heitzinger is Associate Professor at the TU Vienna.
Bibliographic Information
Book Title: Algorithms with JULIA
Book Subtitle: Optimization, Machine Learning, and Differential Equations Using the JULIA Language
Authors: Clemens Heitzinger
DOI: https://doi.org/10.1007/978-3-031-16560-3
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-16559-7Published: 13 December 2022
Softcover ISBN: 978-3-031-16562-7Published: 13 December 2023
eBook ISBN: 978-3-031-16560-3Published: 12 December 2022
Edition Number: 1
Number of Pages: XXI, 439
Number of Illustrations: 2 b/w illustrations, 13 illustrations in colour
Topics: Numerical Analysis, Ordinary Differential Equations, Partial Differential Equations