Skip to main content

Numerical Analysis: A Graduate Course

  • Textbook
  • © 2022


  • Combines theory and practice in an application-based approach
  • Presents accessible graduate-level text
  • Includes algorithms and examples in Matlab and Julia programming

Part of the book series: CMS/CAIMS Books in Mathematics (CMS/CAIMS BM, volume 4)

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 16.99 USD 44.99
Discount applied Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 84.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (8 chapters)


About this book

This book aims to introduce graduate students to the many applications of numerical computation, explaining in detail both how and why the included methods work in practice. The text addresses numerical analysis as a middle ground between practice and theory, addressing both the abstract mathematical analysis and applied computation and programming models instrumental to the field. While the text uses pseudocode, Matlab and Julia codes are available online for students to use, and to demonstrate implementation techniques. The textbook also emphasizes multivariate problems alongside single-variable problems and deals with topics in randomness, including stochastic differential equations and randomized algorithms, and topics in optimization and approximation relevant to machine learning. Ultimately, it seeks to clarify issues in numerical analysis in the context of applications, and presenting accessible methods to students in mathematics and data science. 


“This is an attractive and challenging introduction to the theory and practice of numerical analysis intended primarily as a text for a graduate course. Well-prepared advanced undergraduates might also find it a valuable resource. … Exercises throughout the book are numerous, well tied in to the text, and sometimes very challenging. The author seems to have given considerable thought to their creation and selection. Virtually all the algorithms included in the book come with pseudo-code … .” (Bill Satzer, MAA Reviews, May 16, 2023)

Authors and Affiliations

  • Department of Mathematics, University of Iowa, Iowa, USA

    David E. Stewart

About the author

David Stewart is a Professor of Mathematics at the University of Iowa specializing in the area of numerical analysis. Much of his research work can be found in Dynamics with Inequalities: impacts and hard constraints (SIAM), which is on differential equations with discontinuities. His interests also include numerical optimization, mathematical modeling, and other aspects of differential equations.

Bibliographic Information

  • Book Title: Numerical Analysis: A Graduate Course

  • Authors: David E. Stewart

  • Series Title: CMS/CAIMS Books in Mathematics

  • DOI:

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-031-08120-0Published: 02 December 2022

  • Softcover ISBN: 978-3-031-08123-1Published: 02 December 2023

  • eBook ISBN: 978-3-031-08121-7Published: 01 December 2022

  • Series ISSN: 2730-650X

  • Series E-ISSN: 2730-6518

  • Edition Number: 1

  • Number of Pages: XV, 632

  • Number of Illustrations: 48 b/w illustrations, 66 illustrations in colour

  • Topics: Numerical Analysis, Analysis

Publish with us