CO463/663 Winter 2020
(see: Waterloo LEARN)Convex Optimization and Analysis
- "The great watershed in optimization is not
between linearity and nonlinearity, but convexity and
nonconvexity."
(Rockafellar, 1993.)
Instructor Henry Wolkowicz (MC6312, x35589)
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The course is self-contained but uses:
- basic linear algebra (vector
spaces/inner-products/basic matrix factorizations/eigen-decompositions)
basic calculus (Taylor series/Implicit function Theorem/continuity/convergence).
- Handouts
- Syllabus Course/Marking
- Links/Announcements
- Sample problems/solutions MIT Convex Anal.
- Fundamentals of Lin. Alg. and Opt., ebook
- A new algorithm that minimizes anything/everything
- 200 Best Jobs latest report When the best way to take notes is by hand Matrix Calculus You Need For Deep Learning
- Time:W-F 1:00-2:20PM; from Wed. Jan. 8 to Fri. Apr. 3.
- Location: MC 4064
- Office Hours: Thursday 11:30AM-noon in MC6312 (or after class Wed/Fri)
- TA Office Hour: Tuesday at 10-11 AM in MC5474
- FINAL EXAM Schedule: Take Home Assignment VI/EXAM Due Friday, April 17.
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J.-B. Hiriart-Urruty and C. Lemaréchal, Fundamentals of Convex Analysis, Springer, 2004
(available online)
Latest Course Notes are updated after the lectures and are available at the LEARN webpage.
(Note that these notes are changing during the semester.)Expect 6 assignments - to be submitted at the beginning of the class stated on the assignment.
Last Modified: Saturday 4 April 2020
