Introduction to Linear Algebra for Science and Engineering, 3rd edition

Published by Pearson Canada (January 15, 2019) © 2020

  • Dan Wolczuk University of Waterloo
  • Daniel Norman Queen's University

Paperback + Student Resources

ISBN-13: 9780135282014
Student Solutions Manual for Introduction to Linear Algebra for Science and Engineering
Published 2019

Currently unavailable

Title overview

Norman/Wolczuk’s An Introduction to Linear Algebra for Science and Engineering has been widely respected for its unique approach, which helps students understand and apply theory and concepts by combining theory with computations and slowly bringing students to the difficult abstract concepts. This approach includes an early treatment of vector spaces and complex topics in a simpler, geometric context. An Introduction to Linear Algebra for Science and Engineering promotes advanced thinking and understanding by encouraging students to make connections between previously learned and new concepts and demonstrates the importance of each topic through applications.


This title is also available with Pearson MyLab Mathematics

This title is also available with MyLab Mathematics - an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them better absorb course material and understand difficult concepts.

 

Students, if interested in purchasing this title with MyLab Mathematics, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.


**Supplements are available for download from the MyLab Instructor Resources page. Contact your Pearson rep for access information and instructions if you don’t have a MyLab account.

Table of contents

  1. Euclidean Vector Spaces
  2. Systems of Linear Equations
  3. Matrices, Linear Mappings, and Inverses
  4. Vector Spaces
  5. Determinants
  6. Eigenvectors and Diagonalization
  7. Inner Products and Projections
  8. Symmetric Matrices and Quadratic Forms
  9. Complex Vector Spaces

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