Differences

This shows you the differences between two versions of the page.

--- teaching:wt21:vl:co [2021/10/15 14:17]
ipa created
+++ teaching:wt21:vl:co [2021/10/19 23:08] (current)
ipa [Registration]
@@ Line 5: / Line 5: @@
   * **Lecturer:** [[https://www.stpetra.com|Prof. Stefania Petra]]
   * **Exercises:** Stefania Petra, Matthias Zisler
+  * **Lecture room:** 2.103 (Mathematikon)
+  * **Times:** Wed 11.15-12.45 (lecture), Thu 14.15-15.45 (tutorial)
   * **Format:**
       * In-person lecture were we also explain each subtopic from a top-down viewpoint.
       * Detailed lecture notes that you read on your own.
       * Exercise sheets you work on your own. Solutions will be discussed in person.
-      * We will use Teams for exchanging lecture material.
+      * We will use [[https://teams.microsoft.com/l/team/19%3a6KJMfXAitgm3vq72JEDFEh2zXuwm4RoIWX7yGBZrIvs1%40thread.tacv2/conversations?groupId=943028f7-9974-4480-9a62-3ecea8546719&tenantId=3d057f2f-8466-43fd-8a56-5d334d13d895|Microsoft Teams]] for communication and distributing lecture content.
   * **Language:** English
   * **SWS:** 4
@@ Line 18: / Line 19: @@
   * **Registration:** Please register in Teams.
   * **Prior Knowledge:** Required: Lineare Algebra and Analysis
+==== Registration ====
+If you have not activated your UNI ID for Teams  please use this form
+https://it-service.uni-heidelberg.de/anfrage/teams_benutzer_freischalten\\
+You can use this code for joining Teams: bf3fhns
+==== Content ====
+The lecture gives an introduction into the field of convex optimization and details the most important numerical methods for the solution of convex optimization problems.
+  * //Preliminaries//: Convex sets, convex functions, convex optimization problems (LPs, QPs, SOCPs, SDPs)
+  * //Theory//: Separation theorems, duality, subdifferential calculus, existence and optimality
+  * //Algorithms//: Gradient-based methods for smooth optimization, proximal-point and splitting methods for non-smooth optimization
+  * //Applications//: Convex models in mathematical imaging and data science
+==== Literature ====
+   * R.T. Rockafellar, R.J.-B. Wets, Variational Analysis, Springer, 2004
+   * R. Rockafellar. Convex Analysis. Princeton Univ. Press, 1970
+   * A. Auslender, M. Teboulle, Asymptotic Cones and Functions in Optimization and Variational Inequalities, Springer, 2003
+   * S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004
+   * A. Ben-Tal, A. Nemirovski, Lectures on Modern Convex Optimization, SIAM, 2001
+   * Y. Nesterov. Introductory Lectures on Convex Optimization. Kluwer Acad. Publ., 2004
+   * H. H. Bauschke and P. L. Combettes. Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Springer, 2nd edition, 2017
+==== Exercises ====
+You are supposed to solve at least 50% of the numerical exercises (Matlab, Pyhton) in order to participate in the exam. There will be no points, only “OK”, “Not OK” or “ ” if nothing was handed in
+====== Software Practical: Convex Optimization ======
+The software practical is suited for students attending the lecture on Convex Optimization that additionally wish to apply the algorithms and concepts to concrete examples in order to get a deeper understanding.
+**Registration:** in the lecture, or mail to [[https://www.stpetra.com|Stefania Petra]].