Applied Linear Algebra
MATH5112/6012, Fall semester, 2021
Instructor: Yizao Wang
Email: yizao.wang@uc.edu
Office: 4302 French Hall.
Class Meeting: MWF, 1:25-2:20pm, 60 W Charlton, Room 140.
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),
- 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.
- Orthogonal vectors, bases, matrices, and projections: 4.1–4.4.
- Positive definite matrices and minimization of quadratic functions: 3.4–3.5, 5.2
- Eigenvalues and eigenvectors: 8.2–8.3.
- Linear iterative systems: 9.1–9.2.
and the following applications,
- Cholesky factorization for simulations of Gaussian processes.
- Minimization, least squares, data fitting and interpolation: 4.5, 5.3-5.5.
- Signal processing: 3.6, 5.6.
- Probabilistic and statistical applications: 8.7–8.8.
Homework/Exams/Grades
There will be 4 homework sets, 3 projects (including optional ones 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 (40%) + midterm 1 (15%) + 2 * highest P score (30%) + final (15%) + extra bonus (1 project, 15%)
- MATH6012 students: homework (40%) + midterm 1 (15%) + 2 projects (30%) + final exam (15%) + extra bonus (1 project, 15%)
Tentative Schedule
All homework (HW) due the Monday of the next week. Projects (P) due time see Canvas. All submissions via Canvas.
- Week 1 (Aug 23): Part 1, Basics of matrices, Chapters 1.1-1.6, 1.8-1.9.
- Week 2 (Aug 30): Part 2, Vector spaces and bases, Chapters 2.1-2.5
- Week 3 (Sep 6): Labor Day Holiday, Sep 6, Mon. HW1
- Week 4 (Sep 13): Part 3, Inner products and norms. Chapters 3.1-3.3. HW2 (due Oct 5 now)
- Week 5 (Sep 20):
- Week 6 (Sep 27): Part 4, Orthogonality, Chapters 4.1-4.4.
- Week 7 (Oct 4): Part 5, Positive definite matrix and minimization of quadratic functions, Chapters 3.4-3.5.
- Week 8 (Oct 11): Fall Reading Day, no class on Oct 11. Minimization, least squares, data fitting and interpolation, Chapter 5.2-5.5. HW3.
- Week 9 (Oct 18):
Midterm (materials from HWs 1-3), Friday.
- Week 10 (Oct 25): P1: Cholesky factorization for simulations for Gaussian processes.
P2: Fourier transforms for image processing (JPEG), Chapters 3.6, 5.6.
- Week 11 (Nov 1): Part 7, Eigenvalues and eigenvectors, Chapters 8.2.
- Week 12 (Nov 8): Chapters 8.3, 8.5. HW4.
- Week 13 (Nov 15): P3: Spectral clustering (graph theory).
- Week 14 (Nov 22):
Thanksgiving Weekend Holiday, Nov 26-29. No class on Wed.
- Week 15 (Nov 29): Discussions on projects.
- Examination: Dec 6-Dec 11: Final Exam, TBA.