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Course number and title: EE236B Convex Optimization
Credits: 4
Instructor(s)-in-charge: L. Vanderberghe (vandenbe@ee.ucla.edu)
Course type: Lecture
Required or Elective: A Signals and Systems course.
Course Schedule:
Lecture: 4 hrs/week.
Outside Study: 8 hrs/week.
Office Hours: 2 hrs/week.
 
Course Assessment:
Homework: About 8 homework assignments.
Exams: 1 final exam.
 
Grading Policy: Typically, 30% homework assignments and 70% final exam.
Course Prerequisites: EE236A.
Catalog Description: Basic graduate course in nonlinear programming. Convex sets and functions. Engineering applications and convex optimization. Lagrange duality, optimality conditions, and theorems of alternatives. Unconstrained minimization methods. Convex optimization methods (interior-point methods, cutting-plane methods, ellipsoid algorithms). Lagrange multiplier methods and sequential quadratic programming.  
Textbook and any related course material:
S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
Instructor's notes and other course material available at www.ee.ucla.edu/ee236b.
 
Course Website
Additional Course Website
Topics covered in the course:
Convex sets and functions.
Convex optimization problems.
Linear and quadratic programming.
Geometric programming.
Second-order cone programming.
Semidefinite programming.
Engineering applications of convex optimization.
Lagrange duality.
Methods for unconstrained minimization.
Interior-point methods for constrained optimization.
Will this course involve computer assignments? NO Will this course have TA(s) when it is offered? NO

:: Last modified: January 2010 by L. Vanderberghe ::

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