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.
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.......
Percentage of Course
|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%|