ECE: Electrical & Computer Engineering

ECE 5734 Convex Optimization

Fall 2016 textbook list

The Fall 2016 ECE textbook list is available online for students.

Current Prerequisites & Course Offering

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ECE 5734 Convex Optimization (3C)

Recognizing and solving convex optimization problems. Convex sets, functions, and optimization problems. Least-squares, linear, and quadratic optimization. Geometric and semidefinite programming. Vector optimization. Duality theory. Convex relaxations. Approximation, fitting, and statistical estimation. Geometric problems. Control and trajectory planning.

What is the reason for this course?

From the Preface of Convex Optimization by Stephen Boyd and Lieven Vandenberhghe, page xi: "[C]onvex optimization [is] a special class of mathematical optimization problems, which includes least-squares and linear programming problems...[Convex problems] are more prevalent in practice than was previously thought. Since 1990 many applications have been discovered in areas such as automatic control systems, estimation and signal processing, communications and networks, electronic circuit design, data analysis and modeling,statistics, and finance. Convex optimization has also found wide application in combinatorial optimization and global optimization, where it is used to find bounds on the optimal value as well as approximate solutions.......

Typically offered: Spring. Program Area: Systems/Controls.

Prerequisites: Graduate Standing.

Why are these prerequisites or corequisites required?

The course requires a mathematical maturity that is uncommon in undergraduates, but is expected of graduate students who use computational mathematics.

Department Syllabus Information:

Major Measurable Learning Objectives:
Course Topics
Topic Percentage
1. Convex Sets and Functions. Basic properties and examples; operations that preserve convexity; convexity with respect to generalized inequalities; quasiconvex and log-concave functions. 20%
2. Convex Optimization Problems. Linear, quadratic, and geometric programs; generalized inequalities; semidefinite programming; vector optimization and Pareto optimal points. 20%
3. Optimality Conditions and Duality theory. Lagrange dual problem; weak and strong duality; geometric interpretation; Karush-Kuhn-Tucker conditions; perturbation and sensitivity analysis. 15%
4. Approximation and Fitting. Norm approximation; least-norm problems; regularized approximation; robust approximation. 15%
5. Statistical Estimation. Maximum likelihood estimation; optimal detector design; experiment design. 15%
6. Geometric problems. Extremal volume ellipsoids; centering; classification; facility location. 15%

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