CSE 203B, Winter 2023Convex OptimizationUniversity 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)

- Chen, Danlu, email:dac013@ucsd.edu
- Giri, Vijay, email:vgiri@ucsd.edu
- Holtz, Chester, email:chholtz@ucsd.edu (Lead TA)
- Magee, Lucas, email:lmagee@ucsd.edu
- Singh, Abhishek, email:abs006@ucsd.edu
- Song, Meng, email:mes050@ucsd.edu
Class Platform

- Canvas
- Gradescope
- Piazza
- UCSD Podcast of lectures and discussion sessions
Schedule

- Lectures: 12:30-1:50PM TTH, SOLIS 107
- Discussion: 4:00-4:50PM F, 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)
- Convex Optimization by R. Tibshirani, http://www.stat.cmu.edu/~ryantibs/convexopt/ (related resources)
- EE364a: Convex Optimization I, http://stanford.edu/class/ee364a/ (related resources)
- https://cseweb.ucsd.edu/~kuan/ (CSE203B notes in previous quarters)
PrerequisiteLinear algebra and basic knowledge of numerical methods, or intention of conducting projects related to scientific computation.

ContentWe study the formulations and algorithms solving convex optimization problems. The topics include convex sets, functions, optimality conditions, duality concepts. If time permits, we will talk about gradient descent, conjugate gradient, interior-point methods, and applications. The objective of the course is to provide students 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, pdf.
- Lecture 2 Convex Sets, Reading assignment: Chapter 2, Lecture slides pptx, pdf.
- Lecture 3 Convex Functions, Reading assignment: Chapter 3, Lecture slides pptx, pdf.
- Lecture 4 Formula, Reading assignment: Chapter 4, Lecture slides pptx, pdf, slides with notes Week-4A pdf, slides with notes Week-4B pdf.
- Lecture 5 Duality, Reading assignment: Chapter 5, Lecture slides pptx, pdf.
- Part II: Algorithms

Homework: gradescope submission

- Homework 1, Due 1/18/2023, pdf, tex.
- Homework 2, Due 1/25/2023, pdf, tex.
- Homework 3, Due date shifted to 2/8/2023, note that HW was revised 1/30/2023 pdf, tex.
Discussions

- W1 Discussion (Slides)
- W2 Discussion (Slides)
- W3 Discussion (Slides)
- W4 Discussion (Slides)
- W5 Discussion
- W6 Discussion
- W7 Discussion
Exam

- Take-home exam 2/26-27/2023 (W7-8)
Project