Homepage for CSC 2600, Fall 2010
Topics in Computer Science: Convex Optimization
University of Toronto
YOU SHOULD CHECK THE FOLLOWING ANNOUNCEMENTS REGULARLY
In Question 3 of part A of Assignment 3, you should hand in your program's output and a well-commented copy of the program code.
Assignment 3 is now complete.
Assignment 3 should be handed in at my office, BA4268.
The last class will be on Tuesday December 7.
Assignment 3 is due on Friday Dec 10 (not Dec 3 as originally posted).
All programs in Assignment 3 should be written in Matlab.
According to university policy, assignments can be graded only for those students who are officially enrolled in the course.
Convex optimization is a form of non-linear optimization that
includes linear programming and least squares as special cases. Like
linear programming and least squares, convex optimization has a fairly
complete theory, very efficient algorithms, and a wide range of
applications. Application areas include computer science, engineering,
statistics, finance, economics and operations research.
This course is an introduction to the theory, algorithms and
applications of convex optimization. The goal is to give students a
working knowledge of the subject, i.e., the ability to recognize,
formulate, and solve convex optimization problems. Topics covered will
be selected from the following: convex sets and functions, linear and
quadratic optimization, geometric and semidefinite programming, strong
and weak duality, algorithms for constrained and unconstrained
problems, interior point methods, and applications. The course should
be of special interest to students in machine learning, machine
vision, graphics, numerical analysis, combinatorial optimization and
This course is in area 2C.
Expected work: 3 or 4 homework assignments and possibly a test or exam.
good knowledge of linear algebra and vector calculus.
a willingness to program in Matlab.
Mathematical maturity will be assumed.
Boyd and Vandenberghe,
Cambridge University Press, 2004.
Freely available on the web.
email: bonner [at] cs [dot] toronto [dot] edu
Office: BA 4268
Office hours: by appointment
Tues 1:00-2:30 in BA2139, and Fri 2:00-3:30 in GB221.
There will be no class on Nov 16 and 19.
Note: Some lectures may be only 1 hour in length.
Assignment 3 No more questions will be added.
A quick review of real symmetric matrices.
Dimitri P. Bertsekas, Nonlinear Programming, Athena Scientific, 1999.
David G. Luenberger, Optimization by Vector Space Methods, Wiley, 1969.
R. Tyrell Rockafellar, Convex Analysis, Princeton
University Press. (Available in a 1996 reprint)
Petersen and Pedersen, The Matrix Cookbook.
Lipschutz and Lipson, Schaum's Outline of Linear Algebra.
(very handy, very cheap)
Wrede and Spiegle, Schaum's Outline of Advanced Calculus.
(very handy, very cheap)
Friedberg, Insel and Spence, Linear Algebra, Prentice Hall, 2003.
Prof. Christara's A Brief Introduction to MatLab.
Cleve Moler's Introduction to MATLAB
chapter from his new textbook.
is a good site for Matlab information and tutorials.
good site for Matlab information, tutorials and software.
You may use Octave instead of Matlab for homework assignments.
However, I cannot guarantee to help you if you have problems. Octave
is very similar to Matlab and is freely available on the web, but
the user interface is not as convenient.
for installing and running Octave in Windows.
on installing Octave in Windows.
GNU Octave Repository
Plagiarism and Cheating:
The academic regulations of the University are outlined in the
Code of Behaviour on Academic Matters.
Advice on academic offences