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teaching:ft1920:praktikum:cs [2020/01/22 22:56] ipa [Projects] |
teaching:ft1920:praktikum:cs [2021/03/02 13:28] (current) |
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| The task is to solve numerically the matrix completion problem via the Douglas-Rachford algorithm (see lecture notes) in the noiseless case and via {{ :teaching:ft1920:vl:cs:files:fista.pdf |FISTA}} in a Lagrangian formulation for the noisy case. For theoretical guarantees see {{ :teaching:ft1920:vl:cs:files:matrixcompletion.pdf |Candes, Tao 2010}}. For a concrete problem instance see below. An subset of the [[http://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a|netflix prize data set ]] can also be used. | The task is to solve numerically the matrix completion problem via the Douglas-Rachford algorithm (see lecture notes) in the noiseless case and via {{ :teaching:ft1920:vl:cs:files:fista.pdf |FISTA}} in a Lagrangian formulation for the noisy case. For theoretical guarantees see {{ :teaching:ft1920:vl:cs:files:matrixcompletion.pdf |Candes, Tao 2010}}. For a concrete problem instance see below. An subset of the [[http://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a|netflix prize data set ]] can also be used. | ||
| - | === Faster FISTA for Wavelet Deblurring === | + | === Faster FISTA for Wavelet Deblurring (Taken!) === |
| - | The task ist to implement a fast version of FISTA {{ :teaching:ft1920:vl:cs:files:fasterfista.pdf | Liang, Schönlieb 2019}} and to compare results with the classical version of {{ :teaching:ft1920:vl:cs:files:fista.pdf |FISTA}}. | + | The task is to implement a fast version of FISTA {{ :teaching:ft1920:vl:cs:files:fasterfista.pdf | Liang, Schönlieb 2019}} and to compare results with the classical version of {{ :teaching:ft1920:vl:cs:files:fista.pdf |FISTA}}. |
| The regulizer should be chosen as the l1-norm of the {{ :teaching:ft1920:vl:cs:files:wavelet.m.zip | wavelet}} transformed signal. The linear operator should be given as a blurring operator, see below. Try several blurring masks! | The regulizer should be chosen as the l1-norm of the {{ :teaching:ft1920:vl:cs:files:wavelet.m.zip | wavelet}} transformed signal. The linear operator should be given as a blurring operator, see below. Try several blurring masks! | ||
| - | === <del> FISTA versus the Chambolle-Pock Algorithm for Face Recognition </del> === | + | === FISTA versus the Chambolle-Pock Algorithm for Face Recognition (Taken!) === |
| - | The task ist to compare the performance of {{ :teaching:ft1920:vl:cs:files:fista.pdf |FISTA}} | + | The task is to compare the performance of {{ :teaching:ft1920:vl:cs:files:fista.pdf |FISTA}} |
| to the {{ :teaching:ft1920:vl:cs:files:chambollepock.pdf |Chambolle-Pock}} algorithm on face recognition. | to the {{ :teaching:ft1920:vl:cs:files:chambollepock.pdf |Chambolle-Pock}} algorithm on face recognition. | ||
| To apply FISTA you need to consider the Lagrangian formulation, see e.g. eq. (4.1) in {{ :teaching:ft1920:vl:cs:files:magma.pdf |Hovhannisyan et al, 2016}}. Summarize the convergence results of the more recent work {{ :teaching:ft1920:vl:cs:files:ergodicconvergencecp.pdf |Chambolle-Pock, 2016}}. | To apply FISTA you need to consider the Lagrangian formulation, see e.g. eq. (4.1) in {{ :teaching:ft1920:vl:cs:files:magma.pdf |Hovhannisyan et al, 2016}}. Summarize the convergence results of the more recent work {{ :teaching:ft1920:vl:cs:files:ergodicconvergencecp.pdf |Chambolle-Pock, 2016}}. | ||