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teaching:ft1920:vl:convex [2020/01/08 19:40] ipa code for sheet 9 |
teaching:ft1920:vl:convex [2020/02/11 16:01] ipa [Examination Modalities] |
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====== Convex and Nonlinear Numerical Optimization (MM25) ====== | ====== Convex and Nonlinear Numerical Optimization (MM25) ====== | ||
- | + | ===== Exam ===== | |
- | ===== Announcements ===== | + | <color #ed1c24> |
- | <color #ed1c24>No lecture on Tue, Nov 5 (due to a conference)</color> | + | The exam will take place in the **Lecture Hall 2** (HS2) in the **Physics building** (Kirchhoff-Institut für Physik, KIP, INF 227) on **February 14th 2020** at **14:15**. |
+ | </color> | ||
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==== Place & Time ==== | ==== Place & Time ==== | ||
* **Lecture**: Tuesday and Friday from 11-13 in seminar room 6 in the Mathematikon (INF 205) | * **Lecture**: Tuesday and Friday from 11-13 in seminar room 6 in the Mathematikon (INF 205) | ||
- | * **Exercise class**: Thursday 9-11 in seminar room 7 in the Mathematikon (INF 205), the first exercise class will be on 24th of October. | + | * **Exercise class**: Thursday 9-11 in seminar room 7 in the Mathematikon (INF 205), the first exercise class will be on the 24th of October. |
==== Language ==== | ==== Language ==== | ||
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==== Target Audience ==== | ==== Target Audience ==== | ||
- | Students of mathematics and scientific computing that are interested numerical optimization, with a focus on applications to data analysis and machine learning. | + | Students of mathematics and scientific computing interested in numerical optimization, with a focus on applications to data analysis and machine learning. |
==== Prerequisites ==== | ==== Prerequisites ==== | ||
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We recommend programming the exercises with Python and numpy. | We recommend programming the exercises with Python and numpy. | ||
A basic understanding of Python and numpy should be sufficient for most exercises. | A basic understanding of Python and numpy should be sufficient for most exercises. | ||
+ | |||
+ | |||
+ | ==== Examination Modalities ==== | ||
+ | |||
+ | If you cannot attend the exam on **February 14th 2020**, write a mail to [[:people |Alexander Zeilmann]] until | ||
+ | **February 21th 2020, 23:59** stating that you want to attend a second exam. | ||
+ | |||
+ | The second exam will (most likely) take place around the beginning of the next semester. | ||
+ | So far the date is not fixed. | ||
+ | |||
+ | Further information: | ||
+ | - You can attend at most two exams. | ||
+ | - If you pass your first exam, you cannot attend a second exam. | ||
+ | - You do not have to register for the first exam. Just show up. | ||
+ | - If you do not attend an exam and you hand in a medical attest of sickness in the office of [[:people |Alexander Zeilmann]] up until one week after the planned date of the exam, you can repeat this exam. This holds true for your first and second exam. | ||
+ | - If you do not attend the first exam and do not hand in a medical attest of sickness you can attend the second exam, but there will be no third exam for you. | ||
+ | - There are no auxiliary means allowed. You should bring a pen, your student identity card and your normal identity card (Personalausweis oder Führerschein). | ||
==== Using Mathematica ==== | ==== Using Mathematica ==== | ||
Go to the [[https://www.wolfram.com/programming-lab/|Wolfram Programming Lab]] and click on the orange button. | Go to the [[https://www.wolfram.com/programming-lab/|Wolfram Programming Lab]] and click on the orange button. | ||
- | This brings you to a tutorial notebook. With the file menu in the light grey bar you can create an empty notebook. | + | This brings you to a tutorial notebook. With the file menu in the light grey bar, you can create an empty notebook. |
- | In the notebook you can paste the code from the code files below. | + | In the notebook, you can paste the code from the code files below. |
Execute the code with Shift+Enter. | Execute the code with Shift+Enter. | ||
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{{ :teaching:ft1920:vl:convex:files:coalgorithms-2.pdf |Convex Optimisation Algorithms 2}} \\ | {{ :teaching:ft1920:vl:convex:files:coalgorithms-2.pdf |Convex Optimisation Algorithms 2}} \\ | ||
{{ :teaching:ft1920:vl:convex:files:conjugationduality.pdf |Conjugation, Duality}} \\ | {{ :teaching:ft1920:vl:convex:files:conjugationduality.pdf |Conjugation, Duality}} \\ | ||
- | {{ :teaching:ft1920:vl:convex:files:nonconvex.pdf |Nonconvex Optimization}} | + | {{ :teaching:ft1920:vl:convex:files:nonconvex.pdf |Nonconvex Optimization (update: Jan 21)}} |
+ | |||
===== Exercise Sheets ===== | ===== Exercise Sheets ===== | ||
You need to log in to access the exercise sheets. | You need to log in to access the exercise sheets. | ||
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- {{teaching:ft1920:vl:convex:files:9_StochasticGradientDescent.pdf | Mathematica code for visualizing the stochastic gradient descent for a logistic loss classifier}} | - {{teaching:ft1920:vl:convex:files:9_StochasticGradientDescent.pdf | Mathematica code for visualizing the stochastic gradient descent for a logistic loss classifier}} | ||
- {{teaching:ft1920:vl:convex:files:uebungsblatt10.pdf | Exercise Sheet 10}} | - {{teaching:ft1920:vl:convex:files:uebungsblatt10.pdf | Exercise Sheet 10}} | ||
+ | - {{teaching:ft1920:vl:convex:files:uebungsblatt11.pdf | Exercise Sheet 11}} | ||
+ | - {{teaching:ft1920:vl:convex:files:supportfunctions.pdf | Mathematica code for visualizing support functions}} | ||
+ | - {{teaching:ft1920:vl:convex:files:uebungsblatt12.pdf | Exercise Sheet 12}} | ||
+ | - {{teaching:ft1920:vl:convex:files:data_exercise_sheet_12.zip | Data}} | ||
+ | - {{teaching:ft1920:vl:convex:files:12_nonnegativematrixfactorisation.pdf | Mathematica code for nonnegative matrix factorization}} | ||
+ |