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Abstract
Introduction to Convex Optimization with a focus on algorithms and the numerous applications of Convex Optimization.
Objective
The main goal of this course is to obtain a solid understanding of classical Convex Optimization techniques and their numerous applications, including in Data Science, Machine Learning, and, more generally, in science and engineering. Apart from building up a solid foundational understanding of Convex Optimization, students also get hands-on experience through regular coding exercises. This aims at providing a holistic view on the process of identifying, modeling, and solving a wide range of computational questions that can be cast as Convex Optimization problems.
Content
Key topics include: - Introduction to Convex Optimization. - Subclasses of Convex Optimization: Semidefinite Programming, Second-Order Cone Programming and Geometric Programming. - Applications of Convex Optimization in science and engineering. - Algorithms for Convex Optimization.
Resources
Lecture Notes
A script will be provided.
Literature
- Boyd, S., \& Vandenberghe, L. (2004). Convex Optimization. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511804441