Nonlinear Optimization I 553.761
TTh 4:30pm - 5:45pm, ONLINE VIA ZOOM


Zoom link : Check Blackboard for passcode protected Zoom link.
Instructor : Amitabh Basu
Office Hours : Wednesday 6:30 -- 8:00pm, or email for appointment. Office Hours will be via Zoom. See Blackboard for a passcode protected Zoom link to my virtual office.
Email : basu [dot] amitabh [at] jhu [dot] edu

Teaching Assistant : Dai-Ni Hsieh, Ao Sun and Hongyi Jiang will be the TAs for our class.

Dai-Ni's email is dnhsieh [at] jhu [dot] edu.
Ao's email is asun17 [at] jhu [dot] edu.
Hongyi's email is hjiang32 [at] jhu [dot] edu.
Dai-Ni's office hours will be on Fridays 2:00 -- 3:00pm. See Blackboard for a passcode protected Zoom link to Dai-Ni's office hours.
Ao's office hours will be on Thursdays 8:00 -- 9:00pm. See Blackboard for a passcode protected Zoom link to Ao's office hours.
Hongyi will not have office hours.

Notes/Texts : I will use lecture slides prepared by Daniel P. Robinson for the course. They will be periodically posted here.

Introduction. Slides without "Notes".
Background and basics. Slides without "Notes". Annotations from Sept 1. Annotations from Sept 3. MATLAB demo file
Optimality conditions. Slides without "Notes". Annotations from Sept 3.
Newton's method. Slides without "Notes". Annotations from Sept 8. MATLAB demo file
Convexity. Slides without "Notes". Annotations from Sept 10.
Line Search Methods. Slides without "Notes". Annotations from Sept 15. Annotations from Sept 17. Annotations from Sept 22. Annotations from Sept 24.
In the figure on Slide 59, the "beta" should be an "eta". I am unable to modify the figure because it was made with old software. So I am alerting everyone here.
Conjugate Gradient. Whiteboard from Sept 29. Whiteboard from Oct 1.
Trust Region Methods. Slides without "Notes". Annotations from Oct 6. Annotations from Oct 8.
Least Squares. Slides without "Notes". Whiteboard from Oct 15. Whiteboard from Oct 20.
Smooth Convex Optimization. Whiteboard from Oct 27. Whiteboard from Oct 29.
Nonsmooth Convex Optimization: Section 4.1 from Notes on Convexity. Whiteboard from Nov 3.
Stochastic Gradient Descent. Whiteboard from Nov 3. Whiteboard from Nov 5. Whiteboard from Nov 10. Whiteboard from Nov 12 (Random coordinate minimization).
Coordinate Minimization. Slides without "Notes". Annotations from Nov 17.
Second Order Methods. Slides without "Notes". Annotations from Nov 19. Annotations from Dec 1. Annotations from Dec 3.
Linear Programming. Annotations from Dec 3.

Other useful textbooks and resources

Basic Numerical Analysis:
Basic Real Analysis:
Basic Linear Algebra:
Syllabus : The syllabus with list of topics to be covered is available HERE.
This course considers algorithms for solving various important nonlinear optimization problems and, in parallel, develops the supporting theory. Primary focus will be on unconstrained optimization. Topics will include: necessary and sufficient optimality conditions; gradient, Newton, and quasi-Newton based line-search, and trust-region methods; linear and nonlinear least squares problems; linear and nonlinear conjugate gradient methods; smooth unconstrained convex optimization; stochastic gradient descent. Selected special topics from: coordinate minimization, second-order methods, linear programming.


There will be one take home Midterm and one take home Final exam. In addition, I will regularly (approx. every two week) post homework assigments here. Seriously attempting ALL the homework problems is imperative for your success in the class, and they will give an indication of the kind of problems on the tests.


Midterm and Final Exam Grades