Optimization Videos

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Italic text== Compressive Sampling, Compressed Sensing, University Minnesota ==

1 Emmanuel Candes
2 Chromosome structure, University of California, San Diego
- 2.1 Ronan Fleming
3 International Society for Magnetic Resonance in Medicine (ISMRM Toronto 2008)
- 3.1 Randy Duensing & Feng Huang
4 Convex Optimization, Stanford University
- 4.1 Stephen Boyd

Emmanuel Candes

(requires RealPlayer to watch)

June 4 2007 Sparsity and the l1 norm

June 5 2007 Underdetermined Systems of Linear Equations (Audio begins 4 minutes into film.)

Norms.

Early work by pioneers ( $LaTeX: \approx$ 16 minutes into film).

Deconvolution ( $LaTeX: \approx$ 30 minutes into film).

Lasso, Basis Pursuit ( $LaTeX: \approx$ 38 minutes in).

Wavelets, Curvelets, Ridgelets, sinusoids ( $LaTeX: \approx$ 55 minutes in).

Overcomplete Dictionary ( $LaTeX: \approx$ 57 minutes in).

Basis Pursuit ( $LaTeX: \approx$ 1:03 hours in).

Feature separation ( $LaTeX: \approx$ 1:12 hours in).

Barbara, Jean-Luc Stark ( $LaTeX: \approx$ 1:15 hours in).

Magnetic Resonance Imaging (MRI) ( $LaTeX: \approx$ 1:16 hours in).

Sample rate ( $LaTeX: \approx$ 1:36 hours in).

June 6 2007 Sparsity and Incoherence (If you only watch one Candes video, this is it.)

Recovery of Dirac comb, derivation of minimum sampling rate ( $LaTeX: \approx$ 11 minutes into film).

4:1 sample to sparsity rule ( $LaTeX: \approx$ 21 minutes into film).

Candes' Matlab code ( $LaTeX: \approx$ 25 minutes in).

Fundamental premises of Compressed Sensing: sparsity and incoherence ( $LaTeX: \approx$ 29 minutes in).

June 7 2007 The Uniform Uncertainty Principle

June 8 2007 The Role of Probability in Compressed Sensing

June 11 2007 Part 1 - Robust Compressed Sensing and Connections with Statistics (Audio back at 17 minutes into film.)

June 12 2007 Part 2 - Robust Compressed Sensing and Connections with Statistics

Matlab ( $LaTeX: \approx$ 1:15).

MRI phantom with noise using Dantzig ( $LaTeX: \approx$ 1:28).

Imaging fuel cells ( $LaTeX: \approx$ 1:31).

Subsampling ( $LaTeX: \approx$ 1:36).

June 13 2007 Connections with Information and Coding Theory

error correction (since the beginning).

Matlab decode ( $LaTeX: \approx$ 20 min in).

second error corruption model: gross error + quantization error ( $LaTeX: \approx$ 29 min in).

Connection with the Sparse Recovery Problem ( $LaTeX: \approx$ 57 min in).

Reed-Solomon code ( $LaTeX: \approx$ 1:08 min in).

Matlab for Reed-Solomon code ( $LaTeX: \approx$ 1:26 min in).

June 14 2007 Modern Convex Optimization

Unconstrained Minimization ( $LaTeX: \approx$ 11 min in).

Matlab example for Gradient Descent with exact Line Search ( $LaTeX: \approx$ 19 min in).

Exact line search vs. Backtracking line search ( $LaTeX: \approx$ 22 min in).

Newton Step ( $LaTeX: \approx$ 26 min in).

Self Concordance ( $LaTeX: \approx$ 35 min in).

Equality Constrained Minimization ( $LaTeX: \approx$ 43 min in).

Barrier function ( $LaTeX: \approx$ 47 min in).

Central path ( $LaTeX: \approx$ 53 min in).

Complexity analysis ( $LaTeX: \approx$ 1:14).

Matlab for log-barrier ( $LaTeX: \approx$ 1:25).

Primal-dual interior point methods ( $LaTeX: \approx$ 1:29).

June 15 2007 Topics and Applications of Compressive Sampling

Beyond L1 minimization ( $LaTeX: \approx$ 3 min in).

Reweighted TV for MRI phantom: recover using m=1.2S (S is number of non zero gradient terms) ( $LaTeX: \approx$ 14 min in).

Overcomplete representations ( $LaTeX: \approx$ 19 min in).

Geometric separation: Cartoon + Texture ( $LaTeX: \approx$ 22 min in).

L1 synthesis vs. analysis for CS ( $LaTeX: \approx$ 28 min in).

Pulse reconstruction using L1 synthesis, L1 analysis and reweighted L1 analysis( $LaTeX: \approx$ 36 min).

ADC: nonuniform sampler vs. random pre-integrator ( $LaTeX: \approx$ 48 min).

Chromosome structure, University of California, San Diego

Ronan Fleming

Auto-correlation coefficients (6MB video) from Chromosome structure via Euclidean Distance Matrices.

International Society for Magnetic Resonance in Medicine (ISMRM Toronto 2008)

Randy Duensing & Feng Huang

(requires Adobe Flash Player)

Objective Comparison of Alternate Reconstruction Strategies: An Unmet Need

Username: 44141
Password: Law

Convex Optimization, Stanford University

Stephen Boyd

Tutorials for a graduate level course, 2008

Retrieved from "http://www.convexoptimization.com/wikimization/index.php/Optimization_Videos"

@@ Line 3: / Line 3: @@
 ([http://www.real.com requires RealPlayer to watch])
-[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-4-07.ram June 4 2007] &nbsp;'''Sparsity and the l1 norm.'''
+[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-4-07.ram June 4 2007] &nbsp;'''Sparsity and the l1 norm'''
-[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-5-07.ram June 5 2007] &nbsp;'''Underdetermined Systems of Linear Equations.''' (Audio begins 4 minutes into film.)
+[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-5-07.ram June 5 2007] &nbsp;'''Underdetermined Systems of Linear Equations''' (Audio begins 4 minutes into film.)
 : Norms.
 : Early work by pioneers (<math>\approx</math> 16 minutes into film).
@@ Line 19: / Line 19: @@
 [http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-6-07.ram June 6 2007]
-&nbsp;'''Sparsity and Incoherence.''' (If you only watch one Candes video, this is it.)
+&nbsp;'''Sparsity and Incoherence''' (If you only watch one Candes video, this is it.)
 : 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).
@@ Line 25: / Line 25: @@
 : Fundamental premises of Compressed Sensing: &nbsp;<i>sparsity</i>&nbsp; and &nbsp;<i>incoherence</i>&nbsp; (<math>\approx</math> 29 minutes in).
-[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-7-07.ram June 7 2007] &nbsp;'''The Uniform Uncertainty Principle.'''
+[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-7-07.ram June 7 2007] &nbsp;'''The Uniform Uncertainty Principle'''
-[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-8-07.ram June 8 2007] &nbsp;'''The Role of Probability in Compressed Sensing.'''
+[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-8-07.ram June 8 2007] &nbsp;'''The Role of Probability in Compressed Sensing'''
-[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-11-07.ram June 11 2007] &nbsp;'''Part 1 - Robust Compressed Sensing and Connections with Statistics.''' (Audio back at 17 minutes into film.)
+[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-11-07.ram June 11 2007] &nbsp;'''Part 1 - Robust Compressed Sensing and Connections with Statistics''' (Audio back at 17 minutes into film.)
-[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-12-07.ram June 12 2007] &nbsp;'''Part 2 - Robust Compressed Sensing and Connections with Statistics.'''
+[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-12-07.ram June 12 2007] &nbsp;'''Part 2 - Robust Compressed Sensing and Connections with Statistics'''
 : Matlab (<math>\approx</math> 1:15).
 : MRI phantom with noise using Dantzig (<math>\approx</math> 1:28).

Optimization Videos

From Wikimization

Revision as of 18:00, 27 August 2008

Contents

Emmanuel Candes

Chromosome structure, University of California, San Diego

Ronan Fleming

International Society for Magnetic Resonance in Medicine (ISMRM Toronto 2008)

Randy Duensing & Feng Huang

Convex Optimization, Stanford University

Stephen Boyd

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