Instructor: Avishek Ghosh, Assistant Professor, SysCon and C-MInDS, IIT Bombay
Contact: Room 110, SysCon; Email: avishek_ghosh@iitb.ac.in
Timing: Tuesday/Friday 3:30 - 4:55 pm
Classroom: LT 302, Lecture Hall Complex-2
TA: 1) Ashmita (email: Ashmita@sc.iitb.ac.in)
2) Siddhant (email: siddhant@sc.iitb.ac.in)
3) Aman (email: 214230006@iitb.ac.in)
Scribe Format: Here
Scribe Schedule: Here (Scribes should to sent to the TAs)
About the course: This is a course on Optimisation Theory. It is roughly divided in two parts:
TBD
Grading: HWs (25%), 1 mid term (25%), Final (30%), Scribe (10%) and Class participation (10%)
References:
1) Convex Optimization; S Boyd and L Vandenberghe
2) Numerical Optimization; Nocedal and Wright
3) Optimization for Data Analysis; S. Wright and Ben. Recht (Rough draft available here)
4) Lectures on Convex Optimization; Y. Nesterov
5) Research Papers (will be announced prior to class)
Apart from these resources, we will follow similar courses like: Convex Optimization (EECS 227C, UC Berkeley) by Prof. Martin J. Wainwright, Theoretical Foundations of Data Science II (DSC 40B, UC San Diego) by Prof. Arya Mazumdar, Convex Optimization (EECS 227C, UC Berkeley) by Prof. Jiantao Jiao to name a few.
Lectures:
Scribes will be uploaded as we go.
General Guidelines for Homeworks:
Homeworks should be submitted in class. Students are encouraged to discuss among themselves while solving the HW problems. However, they are required to write the solution on their own. Near-identical submissions will not be awarded any score.