Textbook in PDF format

Numerical Linear Algebra with Julia provides in-depth coverage of fundamental topics in numerical linear algebra, including how to solve dense and sparse linear systems, compute QR factorizations, compute the eigendecomposition of a matrix, and solve linear systems using iterative methods such as conjugate gradient. Julia code is provided to illustrate concepts and allow readers to explore methods on their own. Getting started with Julia Linear algebra bootcamp Linear systems and the basics of floatingpoint arithmetic OR factorization Eigenvalues and eigenvectors Eigenvalue computation for sparse matrices Classical iterations to solve linear systems Krylov methods to solve linear systems A taste of direct methods to solve sparse linear systems Julia essentials Bibliography Index