CSE 203B, Winter 2020 Convex Optimization

University of California, San Diego

Instructor

• CK Cheng, room CSE2130, email: ckcheng+203B@ucsd.edu, tel: 858 534-6184
• Office hour: 3-4PM, Thursday

Teaching Assistant

• Ariel Wang, xiw193@ucsd.edu
• Po-Ya Hus, p8hsu@ucsd.edu
• Fangchen Liu, fliu@ucsd.edu
• Office hours: TBA

Discussion Forum

• http://piazza.com/ucsd/winter2020/cse203b

Schedule

• Lectures: 8:00-9:20AM TTH, Room Centr 119
• Discussion: 8:00-8:50AM F, Room WLH2005
• Midterm Exam: T 2/18/2020 in class

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.
• Funcions of Matrices: Theory and Computation, N.J. Higham, SIAM, 2008.
• Fall 2016, Convex Optimization by R. Tibshirani, http://www.stat.cmu.edu/~ryantibs/convexopt/
• EE364a: Convex Optimization I, S. Boyd, http://stanford.edu/class/ee364a/

Prerequisite

Basic knowledge of numerical methods or intention of conducting projects related to scientific computation.

Content

We study the formulations and algorithms solving convex optimization problems. The topics include convex sets, functions, optimality conditions, duality concepts, 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 pptx, pdf.
• Lecture 2 Convex Set pptx, pdf.
• Lecture 3 Convex Function pptx, pdf.
• Lecture 4 Formulation pptx, pdf.
• Lecture 5 Duality pptx, pdf.
• Part II: Algorithms
  • Lecture 9 Unconstrained Minimization pptx, pdf.
  • Annoucement of course CSE291 Spring 2020 pdf.
  • Lecture 10 Equality Constrained Minimization pptx, pdf.
  • Lecture 11 Interior Point Methods pptx, pdf.

Homework

• Homework 0: Linear Algebra tex, pdf.
• Homework 1: Convex Set tex, pdf.
• Homework 2: Convex Function tex, pdf.
• Homework 3: Problem Formulation tex, pdf. ellipsoid.png. max_flow.png.
• Homework 4: Duality tex, pdf.

Discussion

• Discussion 1: Linear Algebra Review: pdf.
• Discussion 2: Convex Set: pdf. pptx.
• Discussion 3: Convex Functions: pdf. pptx.
• Discussion 4: Convex Functions: pdf. pptx.
• Discussion 5: Convex Problems: pdf. pptx.
• Discussion 6: Midterm Review: pptx.

Exam

• Fall 2017 Midterm review, pdf file
• Fall 2017 Midterm Rubrics, pdf file
• Winter 2019 Midterm Rubrics, pdf file
• Winter 2020 Midterm, pdf file, tex file.
• Winter 2020 Midterm Solution, pdf file.

Project

• Winter 2020 Two Award Winning Projects
• 1. "Learning-based Lyapunov Analysis for Nonlinear Control Systems" by Ya-Chien Chang, Lijing Kuang, and Yuet Fung, pdf file.
• 2. "Convex Optimization for Guided Fluid Simulation" by Owen Jow (pdf NA).
• Winter 2020 Special Award for Timely Subject
• "A Forcasting Model for the Coronavirus (COVID-19) Outbreak in China" by Yizhe Zhao, Guoqiang Liu, Zikun Yang, and Di Wang
• Project Outlines pdf, pptx.
• Outlines (title, motivation, statement of the problem, wish list, task assignment of each member, references) due 2/27/2020.
• Clarification of the project outlines and report (what are we looking for?) Outlines due 2/27/2020. pdf, pptx.
• Outline Sample: Market Forcast docx
• Outline Sample: Spectralization pdf
• Outline Sample: ImageSeg pdf
• Final Presentation Sample pdf
• Final Report Sample: Market Forcast pdf
• Report Sample: Sprectralization pdf
• Report Sample: ImageSeg pdf
• Two award winning teams of the Best Presentation Winter 2019 Award.jpg.
• Project Report (no more than 4 pages) due 11 AM, Th 3/19/2020.