Image from Google Jackets

Linear algebra and its applications / David C. Lay, University of Maryland, College Park, with Steven R. Lay, Lee University, Judith McDonald, Washington State University.

By: Contributor(s): Publisher: Boston, Massachusetts, USA : Pearson Education, Inc., ©2016Edition: Fifth editionDescription: 1 volume, xvi, 494 pages (various pagings) : illustrations (some color) ; 26 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780321982384 (hbk)
  • 032198238X
Subject(s): DDC classification:
  • 512.5 L45 2016 23
LOC classification:
  • QA184.2 .L39 2016
Contents:
Linear equations in linear algebra. -- Matrix algebra. -- Determinants. -- Vector spaces. -- Eigenvalues and eigenvectors. -- Orthogonality and least squares. -- Symmetric matrices and quadratic forms. -- The geometry of vector spaces. -- Optimization (online). -- Finite-state markov chains (online).
Summary: With traditional linear algebra texts, the course is relatively easy for students during the early stages as material is presented in a familiar, concrete setting. However, when abstract concepts are introduced, students often hit a wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations) are not easily understood and require time to assimilate. These concepts are fundamental to the study of linear algebra, so students' understanding of them is vital to mastering the subject. This text makes these concepts more accessible by introducing them early in a familiar, concrete Rn setting, developing them gradually, and returning to them throughout the text so that when they are discussed in the abstract, students are readily able to understand.
Holdings
Item type Current library Collection Call number Status Barcode
Books Books College Library General Circulation Section GC GC 512.5 L45 2016 (Browse shelf(Opens below)) Available HNU000502

Includes index.

Linear equations in linear algebra. --
Matrix algebra. --
Determinants. --
Vector spaces. --
Eigenvalues and eigenvectors. --
Orthogonality and least squares. --
Symmetric matrices and quadratic forms. --
The geometry of vector spaces. --
Optimization (online). --
Finite-state markov chains (online).

With traditional linear algebra texts, the course is relatively easy for students during the early stages as material is presented in a familiar, concrete setting. However, when abstract concepts are introduced, students often hit a wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations) are not easily understood and require time to assimilate. These concepts are fundamental to the study of linear algebra, so students' understanding of them is vital to mastering the subject. This text makes these concepts more accessible by introducing them early in a familiar, concrete Rn setting, developing them gradually, and returning to them throughout the text so that when they are discussed in the abstract, students are readily able to understand.

College of Education Bachelor of Secondary Education major in Mathematics

Text in English

There are no comments on this title.

to post a comment.
Previous
Back to results
Next
Close