TTIC 31070 (CMSC 35470, BUSF 36903, STAT 31015): Convex Optimization

This is a webpage for the Autumn 2015 course at TTIC and the University of Chicago (known as CMSC 35470 at the University).

Tuesdays and Thursdays 10:30am-11:50am at TTIC 530 (located at 6045 S. Kenwood Ave, fifth floor)

Instructor: Nati Srebro.
Additional Lecturer: Ofer Meshi.
TA: Behnam Neyshabur.
Recitations: Wednesdays 4-5pm at TTIC 530
Office Hours: Thursdays 4-5pm at TTIC 501
Homework Submissions: .

Course Description

The course will cover techniques in unconstrained and constrained convex optimization and a practical introduction to convex duality. The course will focus on (1) formulating and understanding convex optimization problems and studying their properties; (2) presenting and understanding optimization approaches; and (3) understanding the dual problem. Limited theoretical analysis of convergence properties of methods will be presented. Examples will be mostly from data fitting, statistics and machine learning.

Specific Topics:

Text Books

The required textbook for the class is: The book is available online here. About 75% of the material covered in the class can be found in the above book.

Supplemental recommended books:

Requirements and Grading:

There will be roughly 7-8 weekly homework assignments, counting toward 50% of the grade. Assignments must be typed (not handwritten) and submitted electronically in PDF. Collaborative work on the homeworks is encouraged, but each student must eventually write up the solution independently. Most assignments also include NumPy programming section. The remaining 50% of the grade will be based on the final exam.

Lectures

Lecture 1: Tuesday, September 29th
Slides
Lecture 2: Thursday, October 1st
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Lecture 3: Tuesday, October 6th
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Lecture 4: Thursday, October 8th
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Lecture 5: Tuesday, October 13th
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Lecture 6: Thursday, October 15th
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Lecture 7: Tuesday, October 20th
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Lecture 8: Thursday, October 22th
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Lecture 9: Tuesday, October 27th
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Lecture 10: Thursday, October 29th
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Lecture 11: Tuesday, November 3rd
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Lecture 12: Thursday, November 6th
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Lecture 13: Tuesday, November 10th
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Lecture 14: Thursday, November 12th
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Lecture 15: Thursday, November 19th
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Lecture 16: Tuesday, November 24th
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Lecture 17: Tuesday, December 1st
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Lecture 18: Thursday, December 3rd
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Assignments