WiSe 16/17: Convex Optimization
Additional information / Pre-requisites
Good background in Linear Algebra. Basic knowledge of Linear Programming is a plus, but is not required.
BMS students, Master and Bachelor studentsclose
Surprisingly many real-world optimization problems can be reformulated as convex optimization problems.
This convexity plays a central role in the computational tractability of a solution.
The goals of this course are
(i) to provide the students with the necessary background to recognize optimization that can be reformulated as convex ones;
(ii) to study the duality theory of convex optimization from the point of view of conic programming, which includes as particular cases the linear programming (LP), semidefinite programming (SDP), second order cone programming (SOCP), and geometric programming (GP);
(iii) to review a variety of applications of convex optimization from various branches such as engineering, control theory, data fitting, statistics and machine learning;
(iv) finally, to understand algorithms for convex programming, in particular interior point methods, and to be able to use modern interfaces to pass optimization problems to solvers that implement these algorithms.
The course is mainly based on:
- Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge university press. (freely available online)
Other useful references may be found in:
- Ben-Tal, A., & Nemirovski, A. (2001). Lectures on modern convex optimization: analysis, algorithms, and engineering applications (Vol. 2). Siam.
- Anjos, M. F., & Lasserre, J. B. (2012). Handbook on semidefinite, conic and polynomial optimization, International Series in Operations Research & Management Science, vol. 166.
16 Class schedule
Additional appointmentsFri, 2017-02-24 10:00 - 12:00