CPSC 406: Computational optimization
Course time and location
Tuesdays and Thursdays 11:00 AM-12:30 PM
Hugh Dempster Pavilion Room 110
Also see
- Piazza: link
Teaching staff
- Yifan Sun (Instructor) Email: ysun13@cs.ubc.ca. Office hours: Monday 3-4pm.
- Zhenan Fan (TA) Email: zhenanf@cs.ubc.ca. Office hours Friday 10-11 am.
- Curtis Fox (TA) Email: curtis.fox@alumni.ubc.ca. Office hours Wednesday 11am-12pm.
- Liran Li (TA) Email: liliran@cs.ubc.ca. Office hours Tuesday 5-6 pm
- Danil Platonov (TA) Email: danil.platonov@alumni.ubc.ca. Office hours Friday5-6pm
Office hours well be held in the DEMCO room in the Xwing of the ICICS building (near the Poke restaurant)
Textbook
Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with MATLAB, Amir Beck (SIAM, 2014). This book is available online through the UBC Library.
Course requirements
One of CPSC 302, CPSC 303, or MATH 307.
Policies
- No makeup exam for the midterm of final. If you missed the midterm exam you must document a justification.
- Midterm exam grade will not be counted if it is lower than your final exam grade.
- To pass the course you must do the assigned coursework, write the midterm and final exams, pass the final exam, and obtain an overall pass average according to the grading scheme.
- The instructors reserve the right to modify the grading scheme at any time.
Past offerings and other resources
Winter Term 2 2018 (Instructor: Prof. Michael Friedlander)
Some material is also borrowed from UCLA's 236A Linear Programming
Lectures
02 Vectors and Matrices Recommended readings: 1.1, 1.2, 1.3,
03 Unconstrained Optimization Recommended readings: 1.5, 2.1, 2.2,, 7.1, 7.2, 7.3, 8.4 (cvx)
04 Quadratic functions Recommended readings: 1.4, 2.3, 2.4,2.5
05 Least Squares Recommended readings: 3.1,3.2
06 Nonlinear least squares Recommended readings: 3.3, 3.4, 3.5, 3.6, 4.5
07 Convex sets and functions: Recommended readings: Chapter 6
08 Gradient descent Recommended readings: 4.1, 4.2, 4.3,
09 Newton's method Recommended readings: 5.1, 5.2, 5.3
10 Not Quite Newton (skipped)
(ordering is shuffled below)
12 Linear programming applications
13 Geometry of linear polytope (same slides as last year)
11 Projected gradient descent Recommended readings: 8.1,8.2, 8.3, 9.1, 9.2, 9.3. Proof of convergence: here
Bonus lecture: A quick survey of online optimization
That's all, folks!