This textbook is intended to introduce advanced undergraduate and
early-career graduate students to the field of numerical analysis. This
field pertains to the design, analysis, and implementation of algorithms
for the approximate solution of mathematical problems that arise in
applications spanning science and engineering, and are not practical to
solve using analytical techniques such as those taught in courses in
calculus, linear algebra or differential equations.Topics covered
include computer arithmetic, error analysis, solution of systems of
linear equations, least squares problems, eigenvalue problems, nonlinear
equations, optimization, polynomial interpolation and approximation,
numerical differentiation and integration, ordinary differential
equations, and partial differential equations. For each problem
considered, the presentation includes the derivation of solution
techniques, analysis of their efficiency, accuracy and robustness, and
details of their implementation, illustrated through the Python
programming language.This text is suitable for a year-long sequence in
numerical analysis, and can also be used for a one-semester course in
numerical linear algebra.