Course: Math 170A: Introduction to Numerical Analysis: Linear Algebra
Class meet time and location: MWF 11am-11:50pm, CENTR 105 Office hours: Wednesday 2-4 pm, location: APM 5755 TA office hours: Qihao Ye, Tuesday 2-4 pm, location: APM 5720 Zihan Shao, Thursday 9:30-11:30 am, location: HSS 5067
Credit Hours: 4 Prerequisites: MATH 18 or MATH 31AH, and MATH 20C or MATH 31BH and CSE 20 or MATH 15A or MATH 31CH or MATH 109. Basic MATLAB skills. Catalog Description: This course covers analysis of numerical methods for linear algebraic systems and least squares problems. Topics include orthogonalization methods. Ill-conditioned problems. Eigenvalue and singular value computations.
Required materials :
- Textbook: Introduction to Numerical Linear Algebra by Christoph Börgers, published by SIAM, 2022. (PDF access on campus Wi-Fi)
- MATLAB: MATLAB (from "matrix laboratory") is a programming language and numerical computing environment commonly used in applied mathematics and other fields of application. Many assignments will involve writing short programs for MATLAB. You can install MATLAB on your personal computer or access it directly through the online version. For more information, please visit the website. An introduction to MATLAB can be found here .
Lecture: Lectures will be held in person at CENTR 105. Lectures will also be recorded and made available on Podcast. Attending the lecture (or reviewing the recording) is a fundamental part of the course; you are responsible for material presented in the lecture whether or not it is discussed in the textbook. You should expect questions on the exams that will test your understanding of concepts discussed in the lecture.
Piazza: A discussion forum for MATH 170. We encourage you to ask questions on Piazza.
Homework: All homework is due on Thursdays 11:59pm (Pacific Time) online, with a late submission deadline on Fridays 11:59pm. Submission is through Gradescope. If you submit after the regular deadline (Thursdays), please include a brief explanation of why you needed additional time. The two lowest scores will be dropped in the end. Coding Policy 1. Please specify your MATLAB version (and add-ons, if any), such as R2023b, within your code as a comment. Alternatively, ensure that your code can be executed using the online MATLAB environment, such as https://www.mathworks.com/products/matlab-online.html or https://octave-online.net. This approach will greatly assist TAs in the review and execution of your code in their own environments. 2. If the primary objective function in your assignment can be easily solved using built-in MATLAB functions without specification, you are encouraged to create your own code implementation. However, you can use built-in functions as verification tools for your code. 3. We urge you to refrain from submitting programs without explanations. The inclusion of comments is highly appreciated. You are expected to elucidate the method/algorithm employed to solve the problem. Additionally, please specify the input/output requirements and the parameters used.
Collaboration policy: You are encouraged to collaborate with other students. However, you must type every line of code and write up all solutions independently. What's encouraged: 1. Discuss homework problems with classmates in person or online. 2. Form study groups to brainstorm ideas and learn together. 3. Collaborate on reading materials or optional class presentations. What’s not allowed: 1. Copying any part of another student’s solution or code. 2. Letting others copy your work. 3. Submitting solutions or code you did not write yourself.
Study groups: Collaborating with classmates is one of the best ways to deepen your understanding of the material. I encourage you to form study groups to work through homework problems, discuss reading assignments, and explore ideas together. Occasionally, I will assign short readings, and you’ll have the opportunity to earn extra credit by giving an optional in-class presentation based on one of them.
Use of AI Tools (e.g., ChatGPT): AI can be helpful, but only if used responsibly. What's allowed: 1. Review background concepts. 2. Check explanations or get hints after attempting the problem. 3. Debug your own code after writing it. 4. Generate extra practice examples to deepen understanding. What’s not allowed: 1. Submitting AI-generated solutions or code as your own. 2. Copying and pasting answers directly into your submission. 3. Using AI to complete homework without understanding the solution. If you use an AI tool to help you understand a concept, acknowledge it briefly in your submission (e.g., “I used ChatGPT to clarify the definition of column space.”) For more information, visit the UCSD Academic Integrity website.
Exams and grading: There will be a one in-class midterm exam on Monday, November 3. Your final grade will be calculated using whichever of the two methods below gives you a higher score: Method 1: (25% HW) + (35% Midterm) + (40% Final). Method 2: (25% HW) + (25% Midterm) + (50% Final). You can earn extra credit by giving an optional class presentation based on reading materials.
Academic Integrity: Academic integrity is highly valued at UCSD and academic dishonesty is considered a serious offense. Students involved in an academic integrity violation will face an administrative sanction which may include suspension or, in very serious cases, expulsion from the university. Your integrity has great value: Cultivate and protect your academic integrity. For more about academic integrity and its value, visit the UCSD Academic Integrity Website.