CSE 203B, Winter 2025 Convex Optimization

University of California, San Diego

Instructor (Office hours TBA in Piazza)

• CK Cheng, room CSE2130, email: ckcheng+203B@ucsd.edu, tel: 858 534-6184

Teaching Assistant (Office hours TBA in Piazza)

• Guo, Enming, email:enguo@ucsd.edu
• Sahu, Kunind, email:kusahu@ucsd.edu
• Wu, Po-Chun, pow001@ucsd.edu
• Jain, Kashish, k4jain@ucsd.edu
• Wahi, Nipun, nwahi@ucsd.edu

Class Platform

• Canvas
• Gradescope
• Piazza
• UCSD Podcast of lectures and discussion sessions

Schedule

• Lectures: 11:00-12:20PM TTH, Peter 108
• Discussion: 8:00-8:50AM W, WLH 2001

References

• Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge, 2004 (Required Textbook).
• Numerical Recipes: The Art of Scientific Computing, Third Edition, W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, Cambridge University Press, 2007 (Recomended Reference)
• Matrix Computations, 4th Edition, G.H. Golub and C.F. Van Loan, Johns Hopkins, 2013 (Recommended Reference)
High-Dimensional Data Analysis with Low-Dimensional Models, J. Wright and Y. Ma, Cambridge 2022 (Recommended Reference)
• https://cseweb.ucsd.edu/~kuan/ (CK Cheng personal website)

Prerequisite

Linear algebra and basic knowledge of numerical methods, or intention of conducting projects related to scientific computation.

Content

We study the formulations and algorithms for solving convex optimization problems. Topics include convex sets, functions, optimality conditions, and duality concepts. If time permits, we will discuss convex optimization algorithms. The course aims to provide students with the background and techniques for scientific computing and system optimization.

Lectures

• Part I: Theory
• Lecture 1 Introduction, Class Logistics, Reading assignment: Chapter 1, Lecture slides pptx file, pdf file, and high level introduction (Reference: Chapter 5): pptx file, pdf file.
• Lecture 2 Convex Sets, Reading assignment: Chapter 2, Lecture slides pptx file, pdf file.
• Lecture 3 Convex Functions, Reading assignment: Chapter 3, Lecture slides pptx file, pdf file.
• Lecture on Matrix Solver, Lecture slides pptx file, pdf file.
• Lecture 4 Formula, Reading assignment: Chapter 4, Lecture slides pptx file, pdf file (1/27/2025).
• Lecture 5 Duality, Reading assignment: Chapter 5, Lecture slides pptx file, pdf file.
• Midterm review, week7-B, no slides.
• Part II: Algorithms
• Lecture 9 Unconstrained Minimization, Reading assignment: Chapter 9, Lecture slides pptx file, pdf file.
• Lecture 10 Equality Constrained Minmization, Reading assignment: Chapter 10. Lecture slides pptx file, pdf file.
• Lecture 11 Interior Point Methods, Reading assignment: Chapter 11. Lecture slides pptx file, pdf file.
• Lecture on Matrix Solver and Conjugate Gradient Method, slides pptm file, pdf file.
• The above files were updated on 3/13/2025.

Homework: gradescope submission

• Homework 1, Due 1/16/2025, pdf file, latex file, Solution: Solution pdf file.
• Homework 2, Updated on 1/15, Due 1/23/2025, pdf file, latex file, Matrix A: A.csv, and vector b: b.csv; Solution: pdf file.
• Homework 3, Due 2/6/2025, pdf file, tex file, Solution (updated 2/23/2025): pdf file.
• Homework 4, Due 2/20/2025 (shifted from 2/13), pdf file, tex file, Solution (updated 2/23/2025): pdf file.

Discussions

• W1 Discussion pdf file
• W2 Discussion pdf file
• W3 Discussion pdf file
• W4 Discussion pdf file
• W5 Discussion pdf file
• The above files were updated on 2/13/2025. The following files are not available for now.
• W6 Discussion pdf file
• W7 Discussion pdf file

Exam

• Take-home exam starting at 10AM on Sunday 2/23/2025, and ending at 10AM on Tuesday 2/25/2025 (W7-8), pdf file, tex file Solution: pdf file.

Project

• Project outlines due 1/31/2025, outline format pdf file, pptx file (W5)
• Report due 2:30PM Th 3/20/2025, Project outline, and report rubrics, pdf file, pptx file (W11)
• Two prototype project reports for Winter 2025 out of a class of 273 students
• Erica Cheng, Michael Lai, Yen-Pu Wang, and You-Yi Wang, "Winning the Votes: A Strategic Optimization Model for Election Campaigns"
• Marshall Fisher, Zhaoyang Jia, Matthew O'Malley-Nichols, Richard Zhang, "Convex Self-Attention Mechanisms"