Math 342 - Linear Algebra II - Winter 2020

Class Information

Instructor: Tuan Pham
TA: Matthias Merzenich
Section 20
Class meetings: Bexell Hall 412, MWF 11:00 - 11:50 AM

[Syllabus]   [Tentative calendar]   [Canvas site]

Office Hours

M, W, F 1:00 - 2:00 PM at Kidder Hall 268
Th 12:00 - 2:00 PM at Kidder Hall 268
W 2:00 - 3:00 PM at Kidder Hall 108 J (computer lab)

Assignments

Homework	Solution (from TA)	Lecture worksheets	Recitation worksheets
Homework 1	Solution	Worksheet 1	Recitation 1
Homework 2	Solution	Worksheet 2	Recitation 2
Homework 3	Solution	Worksheet 3	Recitation 3
Homework 4	Solution	WS 4, WS 5	Recitation 4
		WS 6, WS 7, WS 8	Recitation 5
Homework 5	Solution	WS 9, WS 10	Recitation 6
Homework 6	Solution	Worksheet 11	Recitation 7
Homework 7	Solution	Worksheet 12	Recitation 8
Homework 8	Solution	Worksheet 13	Recitation 9
			Recitation 10

Lecture notes

Review (Mar 13): review for Final exam, link to Zoom video

Lecture 27 (Mar 11): more examples on adjoint operator; singular value decomposition

Lecture 26 (Mar 9): adjoint operator

Lecture 25 (Mar 6): another minimizing problem (least-square method)

Lecture 24 (Mar 4): example of Gram-Schmidt orthogonalization procedure; minimizing problem (cont.)

Lecture 23 (Mar 2): example of Gram-Schmidt orthogonalization procedure; minimizing problem

Lecture 22 (Feb 28): finding orthogonal projection; Gram-Schmidt orthogonalization procedure

Lecture 21 (Feb 26): orthogonal basis and orthogonal projection

Lecture 20 (Feb 24): examples of inner products and norms

Lecture 19 (Feb 21): normed spaces

Lecture 18 (Feb 19): inner product space; norm induced by the inner product

Lecture 17 (Feb 17): basis that diagonalizes a linear map; inner product

Lecture 16 (Feb 14): checking if a linear map is diagonalizable

Lecture 15 (Feb 12): a direct method to find eigenvalues and eigenvectors

Lecture 14 (Feb 7): continue an example of finding eigenvalues and eigenvectors

Lecture 13 (Feb 5): eigenvalues, eigenspaces and eigenvectors; coordinate-based method

Lecture 12 (Feb 3): invariant subspaces

Lecture 11 (Jan 31): direct sum of two or more vector spaces

Lecture 10 (Jan 29): finding basis of the sum of two vector spaces

Lecture 9 (Jan 27): an application of rank-nullity theorem; sum of two vector spaces

Lecture 8 (Jan 24): relations among null space, range space, column space, row space; rank-nullity theorem

Lecture 7 (Jan 22): range space, null space, rank, nullity

Lecture 6 (Jan 17): matrix represenatation of linear maps, null space

Lecture 5 (Jan 15): linear maps

Lecture 4 (Jan 13): basis and dimension

Lecture 3 (Jan 10): subspace, linear combination, spanning set

Lecture 2 (Jan 8): definition of vector spaces and examples

Lecture 1 (Jan 6): introduction

Remarks before / after class

Final review

Linear Algebra Done Right -- videos

Midterm exam solution

Midterm review

Instructions to install Matlab

Links

Department of Mathematics
Oregon State University

This page was last modified on Saturday, March 14, 2020.