.. highlight:: matlab

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EE 732 - Convex Optimization and Engineering Applications
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**Announcement:** 
	- All course information is directed to CourseVille. 

Homework
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Lecture notes
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Acknowledgement: All the handouts are available from the course web of `EE236B <http://www.seas.ucla.edu/~vandenbe/ee236b/ee236b.html>`_ taught by Prof. Lieven Vandenberghe, UCLA. The instructor would like to thank Prof. Vandenberghe for allowing this class to use his lecture notes. 

1. Introduction
2. Convex sets
#. Convex functions
#. Convex optimization problems
#. Duality
#. Approximation and fitting
#. Statistical estimation
#. Geometric problems
#. Numerical linear algebra background
#. Unconstrained minimization
#. Equality constrained minimization
#. Interior-point methods
#. Conclusions


MATLAB files
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	- `data_logistic <./ee732/codes/data_logistic.mat>`_
	- `data_robustapprox <./ee732/codes/data_robustapprox.mat>`_
	- `data_tvdenoising <./ee732/codes/data_tvdenoising.mat>`_
	- `data_svm_train <./ee732/codes/data_svm_train.mat>`_
	- `data_svn_validate <./ee732/codes/data_svm_validate.mat>`_
	

Course Information
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:Lectures: EE 404, Mon Wed 8:00-9:30 AM 

:Instructor: Jitkomut Songsiri (JSS)

:Textbook: We use `Convex Optimization <http://www.seas.ucla.edu/~vandenbe/cvxbook.html>`_ as a main reference book for this course.

- S. Boyd and L. Vandenberghe, Convex Optimization, Oxford, 2004

:Grading: 
	Homework 40%   Midterm 35%  Final 15% Term paper 10%

:Softwares: Most assignments will involve MATLAB programming with optimization toolbox. Students should install `CVX <http://cvxr.com/cvx/>`_ which is a MATLAB package for solving convex programs.