The course meets on Mondays, Wednesdays and Fridays.
Section E: 10:30-11:20am at
DEM 004
Section F: 11:30-12:20pm at
THO 119
Name: Lucas Braune
Website: www.math.uw.edu/~lvhb
E-mail: lvhb 'at' uw.edu
Office: Padelford C-526
Office hours: T-Th, 09:30-11:00am
Click here to see a tentative schedule for the course.
Sep 27 | Linear systems (row and column pictures). Elimination. | Notes |
Sep 29 | Matrix-vector multiplication. Singular systems. Echelon form. | Notes |
Oct 2 | Nonsingular matrices. Leading and free variables. Chemical reactions. | Notes |
Oct 4 | Computational cost. Poisson's equation. Sparse systems. | Notes |
Oct 6 | Abstract vector spaces. Subspaces. | Notes |
Oct 9 | Column space. Span. Nullspace. | Notes |
Oct 11 | Review: computation of N(A) and of particular solutions to Ax=b. | Notes |
Oct 13 | Linear independence. Review of span and nonsingular matrices. | Notes |
Oct 16 | Problems from an old Midterm exam. | |
Oct 18 | Midterm 1 | |
Oct 20 | Linear transformations. | Notes |
Oct 23 | The matrix of a linear transformation. One-to-one and onto. | Notes |
Oct 25 | Composition and matrix multiplication. | Notes |
Oct 27 | The matrix inverse. | Notes |
Oct 30 | Inverse via Gauss-Jordan. LU decomposition. | Notes |
Nov 1 | Inverse of a linear transformation. Basis and dimension. | Notes |
Nov 3 | Bases for row(A), col(A) and null(A). | Notes |
Nov 6 | Determinants: properties. | Notes |
Nov 8 | Determinants: the big formula, cofactor expansions. | Notes |
Nov 10 | No class: Veteran's day (observed) | |
Nov 13 | Review | Notes |
Nov 15 | Midterm 2 | |
Nov 17 | Eigenvalues, eigenvectors. Characteristic polynomial. Trace. | Notes |
Nov 20 | Diagonalization. Powers of a matrix. Fibonacci sequence. | Notes |
Nov 22 | Systems of ODEs. Markov matrices. Steady states. | Notes |
Nov 24 | No class: Thanksgiving Friday | |
Nov 27 | Transposes. Orthogonal vectors and subspaces. | Notes |
Nov 29 | Projections. Least squares. | Notes |
Dec 1 | Least squares. Fitting a line to data points. | Notes |
Dec 4 | Orthonormal bases. Orthogonal matrices. Gram-Schmidt. | Notes |
Dec 6 | QR decomposition. Eigenvalue computations. Fourier series. | Notes |
Dec 8 | Course review. | Notes |
Dec 11 | Final exam (section E) | |
Dec 13 | Final exam (section F) |
The textbook for Math 308 is Linear Algebra with Applications (second edition, with Webassign) by Jeffrey Holt. A loose-leaf version of the book is available at the University Bookstore. Alternatively, it is possible to buy digital access to the book through Webassign.
Homework for this class will be submitted through Webassign. Click here for information on Webassign put together for students by Jennifer Taggart.
The student that wishes practice for the exams may wish to look the Math 308 Exam Archive compiled by Kristin DeVleming. They are advised to bear in mind that exams from previous incarnations of Math 308 may concern material different from what we discuss in the present course.
Homework | 20% |
Midterm 1 | 20% |
Midterm 2 | 20% |
Final Exam | 40% |
The dates of the final exams are set by the university and cannot be changed. The final exam for section E will take place from 8:30 to 10:30am on Monday, December 11, at DEM 004. The final exam for section F will take place from 2:30 to 4:30pm on Wednesday, December 13, at THO 119.
Math 308 is a course on Linear Algebra. At its core, Linear Algebra is about solving systems of linear equations. We will begin by discussing Gaussian elimination, a method that can be used to solve any such system, and also one of the most important algorithms of pure and applied mathematics.
We will interpret a linear system as a single equation $Ax = b$, where $x$ is an unknown vector which, multiplied by a matrix $A$, yields a given vector $b$. This will lead us to the rules of multiplying matrices, and to the notion of a matrix inverse. When the inverse $A^{-1}$ of a matrix $A$ exists, the system $Ax=b$ is easy to solve: $x=A^{-1}b$!
In tandem with the algebra of matrices, we will study their geometry: how do vectors (or planes) move when you multiply them by a matrix? This point of view will lead us to the notion of a linear transformation, which much illuminates matrix multiplication. In carrying out this discussion, we will introduce ourselves to the geometric notions of linear independence, span and dimension of a linear subspace of $\mathbb R^n$.
Next, we will dispel the mystery of determinants. Geometrically, determinants measure volumes. Alternatively, they can be seen as a gadget that takes $n$ size-$n$ vectors and outputs a number. This gadget can be completely understood in terms of three easy-to-remeber rules of algebra.
Using determinants, we will introduce eigenvalues and eigenvectors. In a certain sense, these are numbers and vectors allow us to completely understand any matrix. This point which will be illustrated by applications to recursion problems and systems of ODEs.
To conclude the course, we will discuss lengths and angles. It will be clear that, without much creativity, one can make sense of these notions in dimensions 4 and higher. This will open way to interesting applications. Time permitting, we will discuss least squares regression, a technique that will alow us to answer the question: "What is the line that best approximates 30 given data points?".
(Another landmark application of the notion of orthogonality is Fourier analysis. This is used for example in MP3 compression to discard from an audio signal its components in frequencies that cannot be heard by the human ear. Interested students can talk to me if they want to learn more about this or other applications of linear algebra that we are [unfortunately] unable to cover during lectures. A summary of what the student finds can be worth extra points for them!)
Intersperced with the development of the theory sketched above,
lectures will include as many of the following applications as time
permits: numerical solution of the Laplace equation (heat distribution),
Markov chains (population dynamics and stock markets), and graphs and
networks (circuits, supply chains).