ECE490: Introduction to Optimization
Course Information
Course Description
Basic theory and methods for the solution of optimization problems; iterative techniques for
unconstrained minimization including gradient descent method, Nesterov’s accelerated method,
and Newton’s method; convergence rate analysis via dissipation inequalities; constrained optimization
algorithms including penalty function methods, primal and dual methods, penalty and
barrier method; Lagrangian multiplier theory; duality theory
Required Materials
There is no required textbook for the class. All course material will be presented in class and/or
provided online as notes. The following resources may be used as references.
Grading
Homework: There are roughly biweekly homework assignments (about 7 total). Homework
will be due on at the end of the lecture that is given a week later. Inclass and afterclass
discussions are strongly encouraged. However, copying of others’ homework is not
allowed. Homework problems will include mathematical derivations as well as coding
tasks. Late homework will not be accepted, unless there is a valid reason. Examples of
valid reasons include illnesses, job interviews and travel to conferences to present research
papers. In all such cases, you have to provide proof that you missed the homework deadline
for a valid reason.
Two Midterm Exams: There will be two inclass midterm exams; in roughly the 6th and
12th week, respectively. In both midterms, you are allowed to use notes handwritten on
one 8.5"x11" sheet of paper (you can write on the front and back of the paper).
Grades for the students in Section P4 (4 credits) will be weighted as follows: Homework
(28%), two midterm exam (15% each), Final Exam (20%), and Final Project (22%).
Final Project : The students in Section P4 (4 credits) are required to work independently
on an optimization project and submit a final report. One may use techniques developed
in this course but are also encouraged to learn and apply new techniques. For example,
one can use the dissipation inequality technique developed in the course to investigate
the performance of an optimization algorithm that is not covered in the course. We will
provide candidate algorithms for you to investigate. You can also explore other ideas you
have. More details will be given later in the semester.
