Optimization Videos
From Wikimization
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(→Compressive Sampling, Compressed Sensing, University Minnesota) |
(→Compressive Sampling, Compressed Sensing, University Minnesota) |
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-6-07.ram June 6 2007] | [http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-6-07.ram June 6 2007] | ||
(If you only watch one Candes video, this is it.) | (If you only watch one Candes video, this is it.) | ||
| - | : Recovery of Dirac comb (<math>\approx</math> 11 minutes into film). | + | : Recovery of Dirac comb, derivation of minimum sampling rate (<math>\approx</math> 11 minutes into film). |
: 4:1 <i>sample to sparsity</i> rule (<math>\approx</math> 21 minutes into film). | : 4:1 <i>sample to sparsity</i> rule (<math>\approx</math> 21 minutes into film). | ||
: [[Candes.m|Candes' Matlab code]] (<math>\approx</math> 25 minutes in). | : [[Candes.m|Candes' Matlab code]] (<math>\approx</math> 25 minutes in). | ||
Revision as of 16:46, 31 July 2008
Contents |
Convex Optimization, Stanford
Tutorials for a graduate level course, 2008
Compressive Sampling, Compressed Sensing, University Minnesota
Emmanuel Candes (requires RealPlayer to watch)
June 4 2007 Sparsity.
June 5 2007 Early work and researchers.
June 6 2007 (If you only watch one Candes video, this is it.)
- Recovery of Dirac comb, derivation of minimum sampling rate (
11 minutes into film).
- 4:1 sample to sparsity rule (
- Candes' Matlab code (
- Fundamental premises of Compressed Sensing: sparsity and incoherence (
Chromosome structure, University of California, San Diego
Auto-correlation coefficients (6MB video) from Chromosome structure via Euclidean Distance Matrices.
International Society for Magnetic Resonance in Medicine (ISMRM Toronto 2008)
Objective Comparison of Alternate Reconstruction Strategies: An Unmet Need by Randy Duensing & Feng Huang. (requires Adobe Flash Player)
- Username: 44141
- Password: Law