Numerical Linear Algebra with Julia

This book offers comprehensive insights into numerical linear algebra, covering topics like solving linear systems, QR factorizations, and eigendecomposition, with friendly guidance and practical Julia implementations for students and researchers.

Numerical Linear Algebra with Julia offers a comprehensive exploration of key concepts in numerical linear algebra. The book covers essential topics such as solving both dense and sparse linear systems, performing QR factorizations, computing the eigendecomposition of matrices, and utilizing iterative methods like the conjugate gradient. Its approachable style, complemented by cartoon characters, makes complex subjects more accessible to readers.

Within its pages, the book provides detailed descriptions of algorithms, along with practical implementations in the Julia programming language. This hands-on approach allows readers to engage with the material and experiment with the methods discussed. Additionally, the use of illustrations and graphics helps to clarify core concepts and demonstrate the algorithms in action, enhancing the learning experience.

Targeted at both undergraduate and graduate students, Numerical Linear Algebra with Julia is suitable for a variety of courses, including Advanced Numerical Analysis, Special Topics on Numerical Analysis, and Topics on Data Science, among others. Furthermore, it serves as a valuable reference for researchers in fields such as computational engineering, statistics, data science, and machine learning, all of whom rely on numerical solvers in linear algebra. This book is an essential resource for anyone looking to deepen their understanding of numerical linear algebra and its applications.