All assignments will be postd on Piazza. You will be asked to submit it through Gradescope.
The videos of the lectures will also be available on Illinois Media Space on the ECE 490 Channel .
This is a senior/first year graduate-level course on optimization. Topics include necessary and sufficient conditions for local optima; characterization of convex sets and functions; unconstrained optimization, gradient descent and it variants; constrained optimization and the gradient projection method; optimization with equality and inequality constraints, Lagrange multipliers, KKT conditions; penalty and barrier function methods; weak and strong duality and Slater conditions; augmented Lagrangian methods; sub-gradient methods; proximal gradient descent; applications.
Prerequisites: Linear algebra at the level of Math 415, programming skills at the level of ECE 220.
Textbook: D. Bertsekas. Nonlinear Programming, Athena Scientific, 2016.
S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
D. Luenberger and Y. Ye. Linear and Nonlinear Programming, Springer, 2008.
Grading: Homework = 45%, Midterm = 15%, Porject = 15%, Final = 25%.