• First class is on January 15 at 3:00pm in Towne 309.
  

• Ali Jadbabaie , jadbabai (at) seas (dot) upenn (dot) edu ,
  Office hours : Wednesdays 3:00-5:00pm or by appointment, 365 GRW Moore bldg

• S. P Boyd and L. Vandenberghe, Convex Optimization

• J. Renegar, A Mathematical View of Interior Point Methods for Convex Optimization
  
• A. Ben-Tal and A. Nemirovski, Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications, SIAM, 2001
• D. Bertsekas, A. Nedic, and A Ozdaglar, Convex Analysis and Optimization , 2003.
  

• Homework : 30%
  
• Midterm : 30%
  
• Final : 40%

• Alireza Tahbaz-Salehi Office Hours: Mondays 3:00pm-4:00pm, and Kayhan Batmanghelich , Office hours: Fridays, 1:45pm-2:45pm.

• Some introductory mathematical preliminaries by Professor Mehran Mesbahi
• Linear Algebra Review by Professor Fernando Paganini, UCLA.
These notes contains the minimum amount of linear algebra and analysis required.

• More on convex sets, polytopes, and polyhedra by Professor Jean Gallier

• CVX A Matlab-based software for disciplined convex programing

Date	Lecture	Reading	Contents
January 15	Lecture 1	Chapters1,2	Introduction, Convex Sets
January 20	Lecture 2	Chapters 1,2	Convex Sets
January 22	Lecture 3	Chapter 3	Convex Functions
January 27	Lecture 4	Chapter 3	Convex Functions
January 29	Lecture 5	Chapters 3,4	Convex Optimization Problems
February 3	Lecture 6	Chapter 4	Convex Optimization Problems
February 5	Lecture 7	Chapter 4	Vector Optimization, Conic programming
February 10	Lecture 8	Chapter 5	Duality
February 12	Lecture 9	Chapter 5	Duality in Convex Optimization
February 17	Lecture 10	Chapter 5	Interpretations of duality
February 19	Lecture 11	Chapter 6	Approximation and fitting
February 24	Midterm	Midterm	Midterm
February 26	Lecture 12	Chapters 6,7	Approximation and fitting/ Statistics
March 3	Lecture 13	Chapter 7,8	Geometric Problems, Distance Geometry
March 5	Lecture 14	Notes	Numerical Linear Algebra
March 6-16	Spring Break	Spring Break	Spring Break
March 17	Lecture 15	Chapter 9	Unconstrained Minimization
March 19	Lecture 16	Chapter 9	Unconstrained Minimization
March 24	Lecture 17	Chapter 10	Equality Constrained Minimization
March 26	Lecture 18	Chapter 10	Equality Constrained Minimization
March 31	Lecture 19	Chapter 11	Interior point methods
April 2	Lecture 20	Chapter 11	Interior point Methods
April 7	LECTURE 21	Chapter 11	Complexity of Interior point methods
April 9	Lecture 22	Chapter 9,11	Self Concordant Functions
April 14	Lecture 23	Notes	Advanced topics: SOS optimization
April 26	LECTURE 24	Notes	Sum of Squares Methods
April 24	Lecture 25	Notes	Advanced Topics
April 28	Lecture 26	Notes	Review/Take Home Final