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