Applied Linear Algebra
MATH5112/6012, Fall semester, 2022
Instructor: Yizao Wang
Email: yizao.wang@uc.edu
Office: 4302 French Hall.
Class Meeting: MWF, 1:25-2:20pm, 60 W Charlton, Room 240.
Office Hours: by appointment.
Textbook
- Applied Linear Algebra (2nd Edition), Olver and Shakiban, Springer, 2018.
This book is available from SpringerLink, which means that from UC VPN, at this website, you may
- download a free PDF, or
- (recommended) buy a softcover edition at USD 24.99 (MyCopy, see top-right of the website).
Course Description
We shall cover the following topics from core basic linear algebra (chapter numbers from the textbook),
- (Review) Matrices, vectors, Gaussian Elimination, matrix factorizations, Forward and Back Substitution, inverses, determinants: 1.1–1.6, 1.8–1.9.
- Vector spaces, subspaces, linear independence, bases, dimension: 2.1–2.5.
- Inner products and their associated norms: 3.1–3.3.
- Positive definite matrices and minimization of quadratic functions: 3.4–3.5, 5.2.
- Orthogonal vectors, bases, matrices, and projections: 4.1–4.4.
- Eigenvalues and eigenvectors: 8.2–8.3.
and the following applications,
- Cholesky factorization for simulations of Gaussian processes.
- Signal processing: 3.6, 5.6.
- Spectral clustering (graph theory).
Homework/Exams/Grades
There will be 5 homework sets, 3 projects (including an optional one for extra bonus credit), 1 midterm exam and a final exam. The projects require some minimal programming skills (at your choice of programming languages: Python, R, MATLAB). The total points a student could obtain is 115 = 100 + 15 (bonus) as explained below. The letter grades will be A (90+), A- (85+), B+ (80+), B (75+), etc.
- MATH5112 students: homework (50%) + midterm 1 (15%) + 2 * highest project score (2*max(P1,P2)) (20%) + final (15%) + extra bonus (P3, 10%)
- MATH6012 students: homework (50%) + midterm 1 (15%) + 2 projects (P1+P2) (20%) + final exam (15%) + extra bonus (P3, 10%)
Tentative Schedule
Please check exact due time for homework (HW) and projects (P) at Canvas. All download/submissions via Canvas.
- Week 1 (Aug 22): Part 1, Basics of matrices, Chapters 1.1-1.6, 1.8-1.9.
- Week 2 (Aug 29): P1: Cholesky factorization for simulations for Gaussian processes; Part 2, Vector spaces and bases, Chapters 2.1-2.5. HW1
- Week 3 (Sep 5): Labor Day Holiday, Sep 5, Mon.
- Week 4 (Sep 12): Part 3, Inner products and norms. Chapters 3.1-3.3. HW2
- Week 5 (Sep 19):
- Week 6 (Sep 26): Part 4, Orthogonality, Chapters 4.1-4.4. HW3
- Week 7 (Oct 3): Part 5, Positive definite matrix and minimization of quadratic functions, Chapters 3.4-3.5.
- Week 8 (Oct 10): Fall Reading Day, no class on Oct 10. Midterm (materials from HWs 1-3), Friday.
- Week 9 (Oct 17): Part 6, Minimization, least squares, and data fitting, Chapter 5.2-5.5.
- Week 10 (Oct 24):
Chapters 3.6, 5.6. HW4
- Week 11 (Oct 31): P2: Fourier transforms for image processing (JPEG)
- Week 12 (Nov 7):
- Week 13 (Nov 14): Part 7, Eigenvalues and eigenvectors, Chapters 8.2, 8.3, 8.5.
- Week 14 (Nov 21): HW5.
Thanksgiving Weekend Holiday, Nov 26-29. No class on Wed.
- Week 15 (Nov 28): Expository lectures: Spectral clustering (graph theory).
- Week 16 (Dec 5): Final Exam, TBA.