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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)
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.
Main references to class notes:
Boyd/Vendenberghe Convex Optimizatiion
J.-B. Hiriart-Urruty and C. Lemaréchal,
Fundamentals of Convex Analysis
, Springer, 2004
(available online)
Further References
Latest Course Notes are updated after the lectures and are available at the
LEARN webpage
.
(Note that these notes are changing during the semester.)
NEW Marking Scheme
as of Mar 20 2020: HW 75%; Final Assignment/Assessment 25%.
HOMEWORK
Expect 6 assignments - to be submitted at the beginning of the class stated on the assignment.
No scripting available, tracking aborted.
Last Modified: Saturday 4 April 2020