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The course is meant as sequel to the course "Non-smooth convex optimization methods". It treats more advanced methods and applications of convex optimization, notably structured conic programs, their solution by interior-point methods, and reformulations or approximations of common optimization problems as conic symmetric programs.
The subject of this half-semester course are more advanced methods in convex optimization. It consists of 6 lectures, 2 x 1,5 hours each, and can be seen as continuation of the course "Non-smooth convex optimization methods". Approximate content of each lecture:
Linear algebra: matrices, vector spaces, linear functions
Analysis: differentiability, gradients, convergence, continuity
The course is composed of 18 hours lectures.
Evaluation : A two-hours written exam (E1) in session 1. For those who do not pass there will be another two-hours exam (E2) in session 2.
N1 = Exam1
N2 = Exam2
The exam is given in english only 
The course exists in the following branches:
)Course ID : WMM9AM16
Course language(s):
You can find this course among all other courses.
Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski. Robust Optimization. Princeton University Press, 2009.
Aharon Ben-Tal, Arkadi Nemirovski. Lectures on Modern Convex Optimization - Analysis, Algorithms, and Engineering Applications. Vol. 2 of MPS/SIAM Series on Optimization. SIAM, 2001.
Steven Boyd, Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
Jean-Bernard Lasserre. Moments, Positive Polynomials and their Applications. Vol. 1 of Imperial College Press Optimization Series. Imperial College Press, 2009.
Yurii Nesterov, Arkadi Nemirovski. Interior-Point Algorithms in Convex Programming. Vol. 13 of SIAM Stud. Appl. Math. SIAM, Philadelphia, 1994.
Date of update January 15, 2017
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