
Linear Algebra for Engineers and Scientists, 1/e
Kenneth Hardy, Carleton University
Coming March, 2000 by Prentice Hall Engineering/Science/Mathematics
Copyright 2000, 420 pp.
Cloth
ISBN 0139067280

Sign up for future mailings on this subject.
See other books about:
Introductory Linear AlgebraMathematics
Management of TechnologyMechanical Engineering
Linear AlgebraMechanical Engineering

For a onesemester course in Introductory Linear Algebra.
Although the text has been developed in the context of engineering
and computer science, it is also suitable for science students and
other audiences.
This short text integrates the use of MATLAB in a unique,
innovative way. An early applications chapter is intended to motivate
the study of abstract ideas by the client audience—engineering,
computer science, math, and science students. Linear transformations
are integrated throughout the text.
The most applicationsbased text in this market—Each
chapter pivots from a well chosen application as well as having Chapter
2 filled with applications.
Strong Matlab ties—While the text may be used
without the need of machine computation, Matlab materials are found
in each chapter, as well as Matlab exercises and projects.
Complex numbers are covered more thoroughly than in other
linear algebra texts—Engineers and physics students need strong
coverage of this topic.
Linear transformations are integrated throughout the
text.
Gentle introduction to linear independence and spanning
sets in R^n—An overview is presented in Chapter 1 and the
real meat in Chapter 5.
1. Systems of Linear Equations.
Solution of Linear Systems. Echelon Forms. Rank. Vectors
in R^n. Vector Equations. Spanning Sets. Linear Independence.
2. Applications Systems of Linear Systems.
Scheduling and Optimization, Network Flows. Electrical Circuits.
Curve Fitting.
3. Matrices.
Matrix Algebra. Elementary Matrices. Inverses. LUFactorization.
Applications. Graph Theory. Discrete Dynamical Systems. Markov Chains.
4. Determinants.
5. Vector Spaces.
Subspace. Spanning Sets. Linear Independence. Basis. Coordinates.
Dimension. Change of Basis. Row Space. Column Space. Null Space.
6. Eigenvalues.
Eigenvalues. Eigenvectors. Diagonalization. Powers of Matrices.
Linear Systems of Differential Equations. Linear Recurrence Relations.
7. Orthogonality.
Length and Distance. Orthogonal and Orthonormal Vectors.
Orthogonal Projections. The Gram Schmidt Algorithm. Least Squares.
8. Complex Numbers.
Introduction to Complex Numbers with Applications to Linear
Systems.
9. MATLAB.
Introduction to MATLAB Commands and Graphics.
10. The Tool Chest.
A compilation of useful results and background material.
Glossary.
Index.
Answers to Selected Exercises.
