Linear Algebra

Math 210, Fall 2005

MW 8:30-9:40 and F 8:00-9:10

Ivers 225

Instructor: Dr. Douglas Anderson
  Ivers 234G
  299-4453 (office), 299-4151 (math department)
  andersod@cord.edu

Office Hours: TuTh 1:00-4:00, other times by discovery.

Text: Linear Algebra: a Modern Introduction, 2nd edition, by David Poole, ISBN 0534998453.

Prerequisite: Math 122 (Calculus II) or equivalent.

Purpose: There are many ways to study linear algebra. For example, we could focus on computational matrix algebra and its applications to calculus, differential equations, the physical and social sciences, economics, and so on. Or we could concentrate on the broader topic of linear theory and general vector space structure. These and many other approaches could easily fill several semesters of study. Having only one semester at hand, we will strike a balance between mathematical reasoning on the one hand, and linear systems and vector spaces on the other; a few applications will be done as time permits. With nearly half of our emphasis being theoretical in nature, we consider this course to be the first real transition course to mathematical proof. The reliance on rational, rigorous proof is a hallmark and strength of mathematical inquiry, a reliance we will develop and expect in this course. By studying the mathematical vocabulary and the logical structure of the foundation of linear algebra, we learn the fundamental logic of deductive and inductive reasoning; encounter and construct proofs of elementary theorems using direct, indirect, existence, and inductive arguments; and understand the role of mathematical definitions and counter examples. Topics in linear algebra include systems of linear equations, matrices, determinants, vectors in n-space, abstract vector spaces, and linear transformations.

Homework: Homework will be assigned (even-numbered problems) each day for your development toward mastering the material, and will be due two class periods later. I will allow up to four (4) late submissions without penalty; after that late homework will not be accepted. Your final homework write-up must be neat and organized. You will not only be graded on correct answers but also on the neatness, organization, steps used to derive your answers, and the use of correct English grammar and complete sentences. All students must abide by Concordia College's expectations regarding academic integrity and quality.

Exams:
Attendance is required for all exams. If you should miss an exam for an emergency you will be allowed to make it up only if you have notified me before the exam and it must be made up in a timely manner (to be discussed with me individually). I will give you at least one week notice before the exams. The comprehensive final exam is scheduled for 8:30-10:30 a.m. Tuesday, December 16.

Grading: With a total of 650 points, the course grades will be as follows:



A- 
B+ 

B- 
C+

601-650
581-600
562-580
536-561
516-535
497-515

C
C-
D+
D
D-
F

471-496
451-470
432-450
406-431
386-405
0-385





Homework 
Exam 1 
Exam 2 
Exam 3 
Final Exam

150
100
100
100
200

  every class 
  September 28 
  October 28 
  November 18 
  December 16 (Friday), 8:30



Daily Schedule:
      
Date Section Exercises
      
Sep 2 1.1 Geometry of Vectors (13) 5bc,7-12,14,19-22
      
5 1.2 Vector Dot Product (26) 1-3,7,8,13,14,24,25,30,35,36,42-44
7 Intro to Proof I Handout; (27) 46a,53-59,61-64
9 Intro to Proof II Handout
      
12 Fall Symposium No Class
14 1.3 Lines and Planes (41) 2-4,11-14,21,22,24,27,28,32,41
16 1.4 Code Vectors (53) 1-4,13,14,17,18,22-24,36,37,42,46,53,54a
      
19 2.1 Systems of Equations (64) 21,22,27,31,34-37,40-42
21 2.2 Solving Linear Systems (83) 13,14,26-28,36,37,40-42,45,46,49
23 2.3 Span and Independence (99) 3-5,7-10,15,16,23-25,27,28,42,43,48
      
26 Review (56) 1-20, (132) 1(a)-(e),2-5,7-11,13
28 Exam 1
30 3.1 Matrix Operations (150) 1,5-9,19,20,24,30,33,37,38
      
Oct 3 3.2 Matrix Algebra (159) 3,4,6,10,13,14,22,26,29,37,40
5 3.3 Matrix Inverse I (176) 1-4,6,9,14,17,19,20-23,38
7 3.3 Matrix Inverse II (177) 44,46,51-55
      
10 3.5 Subspace and Basis (207) 1-6,12a,14,19,23,28,29
12 3.5 Dimension and Rank (208) 18,37,39-43,46,47,52,55,64
14 3.6 Linear Transformations (221) 1,4,7,12,16,18,20,21,23,24
      
17 4.1 Eigenvalues and Eigenvectors (259) 1-5,7-9,12,13,15,19,21,24,31,32,36
19 4.2 Determinants (280) 1,4,7,8,12,14,16,47,48,52-56
21 Fall Break No Class
      
24 4.2 Determinants (281) 26,27,30,35-40,45,46
26 Review (250) 1abcghij,2-6,8,9,11,13-16,18,19; (362) 1abcd,2,4,6,7,16
28 Exam 2
      
31 4.3 Eigenvalues and Eigenvectors (295) 3-6,10,17-23,25
Nov 2 4.4 Similarity and Diagonalization (306) 1,5,8-11,20,24,25,31,33,34,40-42
4 4.6 Applications (244) 1-4,10; (356) 1-4,7,8,10
      
7 5.1 Orthogonality (373) 1-6,9,10,14,17,20,22-24,35
9 5.2 Orthogonal Complements (384) 1,2,6,8,10,15,16,19,20
11 5.3 Gram-Schmidt Process (391) 1,2,5,6,7,8
      
14 7.3 Least Squares (595) 7,8,15,32
16 Review (362) 1e-j,9-12,17-20; (429) 1-7,9,11,13,14; (632) 13,14
18 Exam 3
      
21 6.1 Vector Spaces and Subspaces (445) 1,3,5,7,9,24-26,30,31,34,38,40,41,45,62
23 6.2 Independence, Basis, Dimension (460) 3,8,17,20-22,34,37,46,52,54
25 Thanksgiving No Class
      
28 6.3 Change of Basis (475) 1,4,11,16,21
30 6.4 Linear Transformations (484) 1,2,3,6,7,9,15,18,20,22,24,35
Dec 2 6.5 Kernel, 1-to-1 Transformation (499) 1,3,5,7,9,13,15,17,19
      
5 6.5 Range, Onto Transformation (499) 21,27,31,33,37
7 6.6 Matrix of Transformation (516) 1,5,7,13,17,19,23,31,35
9 Review (536) 1-4,7-12,14,18
      
12 Review Exams 1-3
      
16 Final Exam Friday, 8:30-10:30

Key Equivalences in Linear Algebra: Let A be an nxn matrix.

  A inverse exists
  A is invertible
  A is nonsingular
  A(A inverse) = (A inverse)A=I
  rref(A)=I, A and I are row equivalent
  A has n (nonzero) pivots
  Ax=0 has only the trivial solution
  Ax=b has unique solution x=A^(-1)b
  Determinant of A is nonzero
  Rows of A are linearly independent
  Row space of A is R^n
  Rank(A)=n
  Columns of A are linearly independent
  Column space of A is R^n
  Nullity of A is 0
  All eigenvalues of A are nonzero

  A has no inverse
  A is not invertible
  A is singular
  AB not equal to I for any B
  rref(A) has a row of zeros
  A has less than n pivots
  Ax=0 has infinitely-many solutions
  Ax=b has no solution or infinitely many
  Determinant of A is 0
  Rows of A are linearly dependent
  Row space of A has dimension < n
  Rank(A) < n
  Columns of A are linearly dependent
  Column space of A has dimension < n
  Nullity of A > 0
  0 is an eigenvalue of A


  • Online materials for Linear Algebra from Duke University.

  • Douglas Anderson's home page.
  • Mathematics and Computer Science home page.
  • Concordia College home page.

  • Last modified: 8 December 2005