Math 2573H, Fall '01


Last changed: December 14, 2001.

Honors Calculus III.

The course meets 5 times a week: Lectures, 10:10am-10:00am MWF in Nicholson Hall 216, (Max Jodeit, VinH 258, 5-3855, jodeit@math.umn.edu)
and
Discussion sessions at 10:10 in Norris 3 or 12:20 in MechE 18, depending on section; your TA is Kijung Lee (www.math.umn.edu/~kjlee/2573h.html).

The TEXTS are
Multivariable Mathematics, by Richard E. Williamson & Hale F. Trotter,
and
Linear Algebra with Applications, 2nd ed, by Otto Bretscher

Max Jodeit's Office Hours: 1:30 - 2:15 MWF, Otherwise, by appointment -- or call 5-3855 to see if I'm in and available.


Links, if any, to other Web pages or documents related to this course will go here. Announcements, hints, "news" will also be here.

Announcements, hints, "news"

NEWS: The Final Exam is Monday Dec. 17, 1:30pm - 4:30pm, in Armory 202. This Exam is Closed book, Closed notes, and No calculators.

NEWS: Special Problem Gradelines

The gradelines are based on the total of Special Problem scores. Top=pseudo-top=100; A: 80, B: 64, C: 51, D: 42.

NEWS: Weights

Each Test will amount to 18%, the Discussion-session will amount to 23%, the Special Problems will amount to 8%, and the Final will amount to 33%. Only the two highest Test gpa's will be used.


PDF documents

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   Yet another Practice Test.

    An express tour thru determinants. Version 3a, 17 pages

   projection-matrix formulas, review of Gram-Schmidt-2573H...

   Another Practice Test.

   Two rref's are equal iffi their kernels are equal

   A Practice Test.

   Math 2573 Syllabus.


Current reading: Bretscher

We will cover pp 1-38 quickly (examples pp 9-17 have the essence of the Gauss-Jordan method of elimination). We have these goals:
* learn how to reduce a linear system to one that is "upper triangular"
* learn how to reduce an upper triangular system to a "diagonal" one
* learn how to interpret our results in "matrix operation" ways
* learn about kernels, images, and bases for them
* learn how to tell how many solutions a system of linear equations has and how to express them in a simple way

Elementary Row Operations and ERO matrices are discussed on pp 87-88, Exercises #50 -- 56.
Section 2.3 covers invertible matrices: Def 2.3.2, example of
( A | I ) -> ( I | A^{-1} ) on page 68.
When this cannot be done ( A not invertible ) we get
( A | I ) -> ( rref | M ) and MA = rref checks that we made no (net!) mistakes.
The formula for the ij entry of a product of two matrices is on page 78. Why does Bretscher work with BA insread of AB?
Fact 2.4.9 depends on finite dimension! The Right and Left shift operators furnish a counterexample in the infinite-dimensional case.
Partitioned matrices, or "matrices in block form" are discussed on page 82.
Section 3.1 covers kernels and images.



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