SANTA BARBARA CITY COLLEGE

ASSOCIATE DEGREE CREDIT COURSE OUTLINE

 

 

Department:  Mathematics

Subject Area and Course Number:  Mathematics 210

Course Title:   Linear Algebra

Discipline:  Mathematics

Units:  4

Repeatability:  None

Catalog Course Description:  Finite dimensional vector spaces, linear independence, bases, systems of linear equations, linear transformations, matrices, LU factorization, change of bases, similarity of matrices, eigenvalues and eigenvectors, diagonalization, applications, quadratic forms, symmetric and orthogonal matrices, canonical forms, introduction to infinite dimensional vector spaces.

Description for Schedule of Classes: Finite dimensional vector spaces, linear independence, bases, systems of linear equations, linear transformations, matrices, LU factorization, change of bases, similarity of matrices, eigenvalues and eigenvectors, diagonalization, applications.

Lecture Hours per Week:  4.3             (64-72 Total Semester Hours)

Laboratory Hours per Week:  None

Plus Hours:  None

Prerequisites:  Math 160, with a grade of "C" or better.

Co-requisites: None

Skills Advisories:  None

Course Advisories:  None

Limitation on Enrollment:  None

Course Objectives:  Related to the use of matrices and the underlying structure of vector spaces.

1.             Solve Ax=b by elimination, the invertibility of A, or LU factorization.

2.             Determine basis and dimension of vector spaces, especially for column and null spaces of A and AT.

3.             Project a vector onto a subspace.

4.             Use the Gram-Schmidt procedure to factor a matrix into QR form.

5.             Compute determinants using their properties.

6.             Find eigenvalues and eigenvectors of a matrix.

7.             Diagonalize a matrix, compute its powers, and its exponential matrix.

8.             Write quadratic forms as matrix products xTAx using real, symmetric matrices A.

9.             Apply Linear Algebra to problems such as systems of differential equations, finite difference equations, graphs and networks, Markov matrices, Fourier matrix, Fast Fourier Transform, and linear programming.

 

Course Content and Scope 

1.            Systems of Linear Equations and Matrices

                a.            Gaussian Elimination and LU factorization

                b.            Homogeneous Systems of Linear Equations

                c.            Matrices and Matrix Operations

                d.            Rules of Matrix Arithmetic

                e.            Elementary Matrices and a Method for finding A-1

                f.             Further Results on Systems of Equations and Invertibility

2.            Determinants

                a.            The Determinants by Row Reduction

                b .           Evaluating Determinants by Row Reduction

                c.            Properties of the Determinant Function

                d.            Cofactor Expansion; Cramer's Rule

3.            Vectors in 2-Space and 3-Space

                a.            Introduction to Vectors (Geometric)

                b.            Norm of a Vector; Vector Arithmetic

                c.            Dot Product; Projections

                d.            Cross Product

                e.            Lines and Planes in 3-Space

4.            Vector Spaces

                a.            Euclidean n-Space

                b.            General Vector Spaces

                c.            Subspaces

                d.            Linear Independence

                e.            Basis and Dimension

                f.             Row and Column Space; Rank; Finding Bases

                g.            Inner Product Spaces

                h.            Length and Angle in Inner Product Spaces

                i.             Orthonormal Bases; Gram-Schmidt Process; QR factorization

                j.             Coordinates; Change of Basis

5.            Linear Transformations

                a.            Definition of Linear Transformation

                B.           Properties of Linear Transformations; Kernel and Range

                c.            Linear Transformations from Rn to Rm

                d.            Matrices of Linear Transformations

                e.            Similarity

6.            Eigenvalues, Eigenvectors

                a.            Definitions and Introduction

                b.            Diagonalization

                c.            Orthogonal Diagonalization; Symmetric Matrices

7.            Applications

                a.            Approximation Problems; Fourier Series

                b.            Quadratic Forms

                c.            Other applications as time and interest allow

 

Methods of Instruction:  Lecture is the primary activity, along with student problem solving. Students are expected to work outside of class on reading the text and on assigned exercises that may include the use of a computer algebra system, and supplemental reading as determined by the instructor.

 

Required Assignments

1.         Appropriate Readings:  Students are required to read assigned chapters in texts.

2.         Writing Assignments:  Students must work assigned mathematical problems requiring the understanding of abstract ideas.

3.         Appropriate Outside Assignments:  Students will be expected to spend a sufficient amount of time outside of class to practice techniques taught during class time, read assigned materials, and complete frequent homework and computer assignments.

4.         Appropriate Assignments that Demonstrate Critical Thinking:  Students must demonstrate mathematical skills which involve analyzing information, recognizing concepts in new contexts, and drawing analogies. They must also analyze logical arguments for validity and write proofs of their own using both inductive and deductive reasoning within a logical system. Students will also learn to use software tools in solving problems from linear algebra.

 

Methods of Evaluation:  A student's grade will be based on multiple measures of performance in the solving of problems, designing of mathematical models, preparation and analysis of graphs, and analysis of logical arguments.  Such measures will include at least four one-hour exams and a comprehensive final examination requiring demonstrations of problem solving skills.  In addition, instructors may make use of quizzes, written homework assignments, or other appropriate means to judge a student's dexterity with mathematical skills and familiarity with mathematical vocabulary and methods of proof.

 

Appropriate Texts and Supplies

Herman, Pepe, Visual Linear Algebra with Maple and Mathematical Tutorials, Wiley, 2005

Strang, Linear Algebra and its Applications, 4th Ed., Cengage, 2006

 

Student Learning Outcomes:

1.                  Solve Ax=b using a variety of methods such as Gaussian elimination and inverting the matrix A.

2.                  Identify a linear transformation and find the eigenvalues and corresponding eigenspaces.

3.                  Diagonalize a matrix or determine why it is not possible to do so.

4.                  Orthogonalize bases using the Gram-Schmidt process and produce unique representations in terms of these bases.

5.                  Prove Linear Algebra theorems and corollaries.

6.                  Apply Linear Algebra to problems in the sciences.

 

 

JK/mej

Approved December 4, 2006

Revised 5/13/09; 8/24/09

FRC (WPC)