Summary
Optimization is not only a major segment of applied mathematics, it is also a critical problem in many engineering and economic fields. In any situation where resources are limited, decision makers try to solve problems they face in the best possible manner. The course provides theory and practice about a subfield optimization, i.e. convex optimization, where both the objectives and constraints are convex functions or sets.Content
The class will cover topics such as:
Convex sets and functions
Recognizing convex optimization problems
Optimality Conditions and Duality
Linear Programming (geometry of linear programming, applications in network optimization, the simplex method)
Least squares and quadratic programs
Semidefinite programming
Interior point methods
Keywords
Convex Optimisation
Learning Prerequisites
Required courses
A good background in linear algebra. Mastering MATLAB is a plus!
Recommended courses
Basic Linear Algebra
The class usese extensively convex optimization by Stephen Boyd and Steven Vandenberghe, which can be downloaded as a free pdf.
- Professor: Hervé Lebret
