Math 250-02: Introduction to Linear Algebra
Fall 2008

Last Update: December 20, 2008.

Instructor :
Dr. Luis A. Medina
Office :
Hill Center 215
E-mail: lmedina@math.rutgers.edu
Office Hours :
M: 3:00 pm - 4:00 pm, T: 10:00 am - 11:00 am


Place and time.
  • Lecture (Section 02)
    1. MTh2 10:20 AM - 11:40 AM ARC -204 BUS
Useful information:
  1. Syllabus
  2. Book: Elementary Linear Algebra: A Matrix Approach, 2nd Edition, Spence, Insel, & Friedber.
    ISBN # 978-0-13-187141-0.
  3. Homework is mandatory. It will be collected every Thursday.
  4. Final grade will be based on:
    • 5% Homework
    • 15% Quizzes
    • 40% Midterms (2 midterms, each worth 20% of your grade)
    • 40% Final Exam
  5. Rutgers University Interim Academic Integrity Policy
Mid-terms:
  1. Test 1: Monday, October 6, 2008.
  2. Test 2: Monday, November 17, 2008.
Information about final exam:
  1. Time: Monday, Decemeber 22, 2008 from 8:00 AM TO 11:00 AM.
  2. Location: BUS ARC 204. (same room!)
Lecture Schedule: We will follow the following schedule.

Lecture Reading Topics
11.1, 1.2 Matrices, Vectors, and Linear Combinations
2 1.3 Systems of Linear Equations
3 1.4 Gaussian Elimination
4 1.6 Span of a Set of Vectors
5 1.7 Linear Dependence and Linear Independence
6 1.7, 2.1 Homogeneous Systems, Matrix Multiplication
7 2.1 Matrix Algebra
8 2.3, App. E Invertibility and Elementary Matrices,App. E Uniqueness of Reduced Row Echelon Form
9 2.4,2.5 Inverse of a Matrix, Partitioned Matrices and Block Multiplication
10 2.6 LU Decomposition of a Matrix
11 Midterm Exam #1 (In the usual class time and place.)
12 3.1 Determinants; Cofactor Expansions
13 3.2 Properties of Determinants
14 4.1 Subspaces
15 4.2 Basis and Dimension
16 4.3 Column Space and Null Space of a Matrix
17 5.1 Eigenvalues and Eigenvectors
18 5.2 Characteristic Polynomial
19 5.3 Diagonalization of a Matrix
20 5.5 Examples of Diagonalization
21 Midterm Exam #2 (In the usual class time and place.)
22 6.1 Geometry of Vectors; Projection onto a Line
23 6.2 Orthogonal Sets of Vectors; Gram-Schmidt Process; QR factorization
24 6.3 Orthogonal Projection; Orthogonal Complements
25 6.4 Least Squares; Normal Equations
26 6.5, 6.6 Orthogonal Matrices; Diagonalization of Symmetric Matrices
27 6.6 Diagonalization of Quadratic Forms;Spectral Decomposition for Symmetric Matrices
28 Catch up and review

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