Optimization Videos

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[http://videolectures.net/dimitri_bertsekas Polyhedral Approximations in Convex Optimization]
[http://videolectures.net/dimitri_bertsekas Polyhedral Approximations in Convex Optimization]
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<br>
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==Convex Optimization, Stanford==
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==Numerics of Convex Optimization, Stanford==
===Gene Golub===
===Gene Golub===
[http://videolectures.net/mlws04_gene_nmsls Numerical Methods for Solving Least Squares Problems with Constraints]
[http://videolectures.net/mlws04_gene_nmsls Numerical Methods for Solving Least Squares Problems with Constraints]
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<br>
==Compressive Sampling and Frontiers in Signal Processing==
==Compressive Sampling and Frontiers in Signal Processing==
=== Compressive Sampling, Compressed Sensing - Emmanuel Candes (California Institute of Technology) University of Minnesota, Summer 2007 ===
=== Compressive Sampling, Compressed Sensing - Emmanuel Candes (California Institute of Technology) University of Minnesota, Summer 2007 ===
([http://www.real.com requires RealPlayer to watch])
([http://www.real.com requires RealPlayer to watch])
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[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'''
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====[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'''====
: Example of sparse signals in genomics (<math>\approx</math> 8 minutes into film).
: Example of sparse signals in genomics (<math>\approx</math> 8 minutes into film).
: Example of sparse signals in genetics (<math>\approx</math> 11 min in).
: Example of sparse signals in genetics (<math>\approx</math> 11 min in).
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: Computational harmonic analysis (<math>\approx</math> 1:22 in).
: Computational harmonic analysis (<math>\approx</math> 1:22 in).
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[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.)
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====[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'''====
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(Audio begins 4 minutes into film.)
: Norms.
: Norms.
: Early work by pioneers (<math>\approx</math> 16 minutes into film).
: Early work by pioneers (<math>\approx</math> 16 minutes into film).
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: ''Barbara'', Jean-Luc Stark (<math>\approx</math> 1:15 hours in).
: ''Barbara'', Jean-Luc Stark (<math>\approx</math> 1:15 hours in).
: Magnetic Resonance Imaging (MRI) (<math>\approx</math> 1:16 hours in).
: Magnetic Resonance Imaging (MRI) (<math>\approx</math> 1:16 hours in).
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: High total variation in MRI Shepp-Logan phantom (<math>\approx</math> 1:25 hours in).
: Sample rate (<math>\approx</math> 1:36 hours in).
: Sample rate (<math>\approx</math> 1:36 hours in).
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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]
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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] &nbsp;'''Sparsity and Incoherence'''====
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&nbsp;'''Sparsity and Incoherence''' (If you only watch one Candes video, this is it.)
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(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).
: 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).
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: Fundamental premises of Compressed Sensing: &nbsp;<i>sparsity</i>&nbsp; and &nbsp;<i>incoherence</i>&nbsp; (<math>\approx</math> 29 minutes in).
: Fundamental premises of Compressed Sensing: &nbsp;<i>sparsity</i>&nbsp; and &nbsp;<i>incoherence</i>&nbsp; (<math>\approx</math> 29 minutes in).
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[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'''
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====[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'''====
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[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'''
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====[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'''====
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[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.)
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====[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'''====
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(Audio back at 17 minutes into film.)
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[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'''
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====[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).
: Matlab (<math>\approx</math> 1:15).
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: MRI phantom with noise using Dantzig (<math>\approx</math> 1:28).
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: MRI Shepp-Logan phantom with noise using Dantzig (<math>\approx</math> 1:28).
: Imaging fuel cells (<math>\approx</math> 1:31).
: Imaging fuel cells (<math>\approx</math> 1:31).
: Subsampling (<math>\approx</math> 1:36).
: Subsampling (<math>\approx</math> 1:36).
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-13-07.ram June 13 2007] &nbsp;'''Connections with Information and Coding Theory'''
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====[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-13-07.ram June 13 2007] &nbsp;'''Connections with Information and Coding Theory'''====
: error correction (since the beginning).
: error correction (since the beginning).
: Matlab decode (<math>\approx</math> 20 min in).
: Matlab decode (<math>\approx</math> 20 min in).
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: Matlab for Reed-Solomon code (<math>\approx</math> 1:26 min in).
: Matlab for Reed-Solomon code (<math>\approx</math> 1:26 min in).
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-14-07.ram June 14 2007] &nbsp;'''Modern Convex Optimization'''
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====[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-14-07.ram June 14 2007] &nbsp;'''Modern Convex Optimization'''====
: Unconstrained Minimization (<math>\approx</math> 11 min in).
: Unconstrained Minimization (<math>\approx</math> 11 min in).
: Matlab example for Gradient Descent with exact Line Search (<math>\approx</math> 19 min in).
: Matlab example for Gradient Descent with exact Line Search (<math>\approx</math> 19 min in).
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: Primal-dual interior point methods (<math>\approx</math> 1:29).
: Primal-dual interior point methods (<math>\approx</math> 1:29).
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-15-07.ram June 15 2007] &nbsp;'''Topics and Applications of Compressive Sampling'''
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====[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-15-07.ram June 15 2007] &nbsp;'''Topics and Applications of Compressive Sampling'''====
: Beyond L1 minimization (<math>\approx</math> 3 min in).
: Beyond L1 minimization (<math>\approx</math> 3 min in).
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: Reweighted TV for MRI phantom: recover using m=1.2S (S is number of non zero gradient terms) (<math>\approx</math> 14 min in).
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: Reweighted TV for MRI Shepp-Logan phantom: recover using m=1.2S (S is number of non zero gradient terms) (<math>\approx</math> 14 min in).
: Overcomplete representations (<math>\approx</math> 19 min in).
: Overcomplete representations (<math>\approx</math> 19 min in).
: Geometric separation: Cartoon + Texture (<math>\approx</math> 22 min in).
: Geometric separation: Cartoon + Texture (<math>\approx</math> 22 min in).
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: Universal encoder (<math>\approx</math> 1:16 min).
: Universal encoder (<math>\approx</math> 1:16 min).
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/discussion6-6-07.ram June 6, 2007] &nbsp;'''Discussion Session'''
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===[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/discussion6-6-07.ram June 6, 2007] &nbsp;'''Discussion Session'''===
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<br>
== Introduction to Magnetic Resonance Imaging (MRI) ==
== Introduction to Magnetic Resonance Imaging (MRI) ==
=== Leon Axel (New York University), Steen Moeller (University of Minnesota) ===
=== Leon Axel (New York University), Steen Moeller (University of Minnesota) ===
[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/axel-moeller6-5-07.ram June 5, 2007]
[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/axel-moeller6-5-07.ram June 5, 2007]
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<br>
==Compressive Sampling, Compressed Sensing==
==Compressive Sampling, Compressed Sensing==
=== Richard Baraniuk (Rice University) Summer 2007 ===
=== Richard Baraniuk (Rice University) Summer 2007 ===
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/baraniuk6-13-07.ram June 13, 2007] &nbsp;'''Compressive imaging with a single pixel camera'''
[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/baraniuk6-13-07.ram June 13, 2007] &nbsp;'''Compressive imaging with a single pixel camera'''
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<br>
==Compressive Sampling, Compressed Sensing==
==Compressive Sampling, Compressed Sensing==
=== Ronald DeVore (University of South Carolina) Summer 2007 ===
=== Ronald DeVore (University of South Carolina) Summer 2007 ===
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/devore6-15-07.ram June 15, 2007] &nbsp;'''Open Problems'''
[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/devore6-15-07.ram June 15, 2007] &nbsp;'''Open Problems'''
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<br>
==Compressive Sampling, Compressed Sensing==
==Compressive Sampling, Compressed Sensing==
=== Anna Gilbert (University of Michigan) Summer 2007 ===
=== Anna Gilbert (University of Michigan) Summer 2007 ===
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/gilbert6-8-07.ram June 8, 2007] &nbsp;'''Algorithms for Compressed Sensing, II'''
[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/gilbert6-8-07.ram June 8, 2007] &nbsp;'''Algorithms for Compressed Sensing, II'''
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<br>
==Compressive Sampling, Compressed Sensing==
==Compressive Sampling, Compressed Sensing==
=== Presentations by Participants, University of Minnesota, Summer 2007 ===
=== Presentations by Participants, University of Minnesota, Summer 2007 ===
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[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/presentations5.6-14-07.ram June 14, 2007] Open Problems in Compressed Sensing
[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/presentations5.6-14-07.ram June 14, 2007] Open Problems in Compressed Sensing
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<br>
== Chromosome structure, University of California, San Diego ==
== Chromosome structure, University of California, San Diego ==
===Ronan Fleming===
===Ronan Fleming===
[http://www.convexoptimization.com/TOOLS/Multimodal_Empirical_Image_Correlation_Decomposition_Fleming.avi Auto-correlation coefficients (6MB video)] &nbsp;from [[Chromosome structure via Euclidean Distance Matrices]].
[http://www.convexoptimization.com/TOOLS/Multimodal_Empirical_Image_Correlation_Decomposition_Fleming.avi Auto-correlation coefficients (6MB video)] &nbsp;from [[Chromosome structure via Euclidean Distance Matrices]].
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<br>
== International Society for Magnetic Resonance in Medicine (ISMRM Toronto 2008) == <!-- Don't change title because an ebook links here. -->
== International Society for Magnetic Resonance in Medicine (ISMRM Toronto 2008) == <!-- Don't change title because an ebook links here. -->
===Randy Duensing &amp; Feng Huang===
===Randy Duensing &amp; Feng Huang===
'''('''requires [http://www.macromedia.com/go/getflashplayer Adobe Flash Player]''')'''
'''('''requires [http://www.macromedia.com/go/getflashplayer Adobe Flash Player]''')'''
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[http://cds.ismrm.org/protected/08Presentations/1350WSSUnsolved/1350_Huang_controller.swf Objective Comparison of Alternate Reconstruction Strategies: An Unmet Need]
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[http://cds.ismrm.org/protected/08PresentationsE/1350WSSUnsolved Objective Comparison of Alternate Reconstruction Strategies: An Unmet Need]
* Username: 44141
* Username: 44141
* Password: Law
* Password: Law
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<br>
== Convex Optimization, Stanford University==
== Convex Optimization, Stanford University==
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[http://www.stanford.edu/class/ee364b/videos.html Convex Optimization II]
[http://www.stanford.edu/class/ee364b/videos.html Convex Optimization II]
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<br>
==International Conference on Machine Learning (ICML July 2008)==
==International Conference on Machine Learning (ICML July 2008)==
===Yoram Singer===
===Yoram Singer===
[http://videolectures.net/icml08_singer_ep Efficient Projections onto the L1-Ball for Learning in High Dimensions]
[http://videolectures.net/icml08_singer_ep Efficient Projections onto the L1-Ball for Learning in High Dimensions]
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<br>
==A Plenary Talk given at the SIAM Annual Meeting, Boston 2006==
==A Plenary Talk given at the SIAM Annual Meeting, Boston 2006==
===Timothy A. Davis===
===Timothy A. Davis===
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[http://www.cise.ufl.edu/research/sparse/SIAM06 University of Florida Department of Computer and Information Science and Engineering]
[http://www.cise.ufl.edu/research/sparse/SIAM06 University of Florida Department of Computer and Information Science and Engineering]
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<br>
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== Compressed Sensing Invited Lectures (March 2011), University of Cambridge==
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===Emmanuel Candes===
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[http://www.sms.cam.ac.uk/collection/1117766;jsessionid=E08892ABBCC13F860DD558D74C857595 8 lectures on Compressed Sensing]

Current revision

Contents

Convex Optimization, MIT

Dimitri Bertsekas

Polyhedral Approximations in Convex Optimization


Numerics of Convex Optimization, Stanford

Gene Golub

Numerical Methods for Solving Least Squares Problems with Constraints


Compressive Sampling and Frontiers in Signal Processing

Compressive Sampling, Compressed Sensing - Emmanuel Candes (California Institute of Technology) University of Minnesota, Summer 2007

(requires RealPlayer to watch)

June 4 2007  Sparsity and the l1 norm

Example of sparse signals in genomics (LaTeX: \approx 8 minutes into film).
Example of sparse signals in genetics (LaTeX: \approx 11 min in).
Example of sparse signals in audio/image processing (LaTeX: \approx 18 min in).
Transform-domain image coding (LaTeX: \approx 27 min in).
Primary visual cortex (LaTeX: \approx 53 min in).
Efficient estimation (LaTeX: \approx 57 min in).
Computational harmonic analysis (LaTeX: \approx 1:22 in).

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).
High total variation in MRI Shepp-Logan phantom (LaTeX: \approx 1:25 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 Shepp-Logan 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 Shepp-Logan 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).
Universal encoder (LaTeX: \approx 1:16 min).

June 6, 2007  Discussion Session


Introduction to Magnetic Resonance Imaging (MRI)

Leon Axel (New York University), Steen Moeller (University of Minnesota)

June 5, 2007


Compressive Sampling, Compressed Sensing

Richard Baraniuk (Rice University) Summer 2007

June 11, 2007  Compressive sensing for time signals: Analog to information conversion

June 12, 2007  Compressive sensing for detection and classification problems

June 12, 2007  Multi-signal, distributed compressive sensing

June 13, 2007  Compressive imaging with a single pixel camera


Compressive Sampling, Compressed Sensing

Ronald DeVore (University of South Carolina) Summer 2007

June 4, 2007  Signal encoding

June 5, 2007  Compression

June 6, 2007  Discrete compressed sensing

June 7, 2007  The Restricted Isometry Property

June 8, 2007  Construction of CS matrices with best Restricted Isometry Property

June 11, 2007  Performance of CS matrices revisited

June 12, 2007  Performance in probability

June 13, 2007  Decoders

June 14, 2007  Performance of iterated least squares

June 15, 2007  Open Problems


Compressive Sampling, Compressed Sensing

Anna Gilbert (University of Michigan) Summer 2007

June 7, 2007  Algorithms for Compressed Sensing, I

June 8, 2007  Algorithms for Compressed Sensing, II


Compressive Sampling, Compressed Sensing

Presentations by Participants, University of Minnesota, Summer 2007

June 4, 2007 (Audio begins 31 seconds into film.)

June 14, 2007 MRI

June 14, 2007

June 14, 2007

June 14, 2007 Dental Tomography

June 14, 2007 Open Problems in Compressed Sensing


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

Convex Optimization I

Convex Optimization II


International Conference on Machine Learning (ICML July 2008)

Yoram Singer

Efficient Projections onto the L1-Ball for Learning in High Dimensions


A Plenary Talk given at the SIAM Annual Meeting, Boston 2006

Timothy A. Davis

Direct Methods for Sparse Linear Systems: The MATLAB sparse backslash.

University of Florida Department of Computer and Information Science and Engineering


Compressed Sensing Invited Lectures (March 2011), University of Cambridge

Emmanuel Candes

8 lectures on Compressed Sensing

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