CSE 203B, Winter 2026 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)

• Harshvardhan, Harsh, hharshvardhan@ucsd.edu
• Wang, Yucheng, yuw132@ucsd.edu
• Yu, Chihao, chy007@ucsd.edu
• Yuan, Ying, yiy090@ucsd.edu

Class Platform

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

Schedule

• Lectures: 8:00-9:20PM TTH, MOS 0114
• Discussion: 11:00-11:50AM F, MOS 0113

References

• Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge, 2004 (Required Textbook).
• Linear and Nonlinear Programming, D.G. Luenberger and Y. Ye, Springer, Fifth Edition, 2022. (Recommended Reference)
• Numerical Optimization, Springer, J. Nocedal and S.J. Wright, Sencod Edition, 2006. (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, duality concepts, and 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 4 Formula, Reading assignment: Chapter 4, Lecture slides pptx file, pdf file (1/22/2026).
• Lecture 5 Duality, Reading assignment: Chapter 5, Lecture slides pptx file, pdf file (2/5/2026).
• Midterm Review W7B, outlines (2/17/2026).
• Part II: Algorithms
• Lecture 9 Unconstrained Minimization, Reading assignment: Chapter 9, Lecture slides pptx file, pdf file (2/24, W8A).
• 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.

Homework: gradescope submission

• Homework 1, Due date was 1/16/2026, and is now shited to 1/19/2026 to accommodate students who get into Canvas system late, pdf file, latex file, Solution in pdf format/a>.
• Homework 2, Due 1/23/2026, pdf file, latex file, Solution in pdf format.
• Homework 3, Due 2/6/2026, pdf file, latex file, HW3 Solution.
• Homework 4, Due 2/13/2026, pdf file, latex file, HW4 Solution.

Discussions

• Discussion 1 Review of Linear Algebra pdf file, pptx file (W1)
• Discussion 2 Convex Set pdf file.
• Discussion 3 Convex Set pdf file.
• Discussion 4 Convex Function pdf file.
• Discussion 5 Convex Function pdf file.
• Discussion 6 Convex Function pdf file.
• Discussion 7 Convex Function pdf file.

Exam

• In-person in-class exam on Th 2/19/2026.

Project

• Project outlines due 1/30/2026, outline format pdf file, pptx file (W4)
• Report due 10 PM Wednesday 3/18/2026, Project outline, and report rubrics, pdf file, pptx file (W11)
• We list two prototype project reports out of 43 reports. One more selected report opts not to be listed here for future publication.
• Felicia Tao, Haoyang Li, Samuel Lin, and Vincent McCloskey, "Robust LLM routing via second-order cone programming under distributional uncertainty," pdf file
• Samintha Chandrasiri, Anirudh Sriram, and Siddhant Mantri, "Distributionally robust optimization with feature-weighted transport costs," pdf file