Optimization Videos

From Wikimization

(Difference between revisions)
Jump to: navigation, search
(Compressive Sampling and Frontiers in Signal Processing)
Line 1: Line 1:
-
==Compressive Sampling and Frontiers in Signal Processing==
+
RuFZx1 gks72nf95mdHfLav1Xpu
-
=== 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.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/candes6-4-07.ram June 4 2007]  '''Sparsity and the l1 norm'''
+
-
: 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 audio/image processing (<math>\approx</math> 18 min in).
+
-
: Transform-domain image coding (<math>\approx</math> 27 min in).
+
-
: Primary visual cortex (<math>\approx</math> 53 min in).
+
-
: Efficient estimation (<math>\approx</math> 57 min in).
+
-
: Computational harmonic analysis (<math>\approx</math> 1:22 in).
+
-
 
+
-
[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).
+
-
: Deconvolution (<math>\approx</math> 30 minutes into film).
+
-
: Lasso, Basis Pursuit (<math>\approx</math> 38 minutes in).
+
-
: Wavelets, Curvelets, Ridgelets, sinusoids (<math>\approx</math> 55 minutes in).
+
-
: Overcomplete Dictionary (<math>\approx</math> 57 minutes in).
+
-
: Basis Pursuit (<math>\approx</math> 1:03 hours in).
+
-
: Feature separation (<math>\approx</math> 1:12 hours in).
+
-
: ''Barbara'', Jean-Luc Stark (<math>\approx</math> 1:15 hours in).
+
-
: Magnetic Resonance Imaging (MRI) (<math>\approx</math> 1:16 hours in).
+
-
: Sample rate (<math>\approx</math> 1:36 hours in).
+
-
 
+
-
[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.)
+
-
: 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).
+
-
: [[Candes.m|Candes' Matlab code]] (<math>\approx</math> 25 minutes in).
+
-
: 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/candes6-8-07.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/candes6-12-07.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).
+
-
: Imaging fuel cells (<math>\approx</math> 1:31).
+
-
: Subsampling (<math>\approx</math> 1:36).
+
-
 
+
-
[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).
+
-
: Matlab decode (<math>\approx</math> 20 min in).
+
-
: second error corruption model: gross error + quantization error (<math>\approx</math> 29 min in).
+
-
: Connection with the Sparse Recovery Problem (<math>\approx</math> 57 min in).
+
-
: Reed-Solomon code (<math>\approx</math> 1:08 min in).
+
-
: Matlab for Reed-Solomon code (<math>\approx</math> 1:26 min in).
+
-
 
+
-
[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).
+
-
: Matlab example for Gradient Descent with exact Line Search (<math>\approx</math> 19 min in).
+
-
: Exact line search ''vs.'' Backtracking line search (<math>\approx</math> 22 min in).
+
-
: Newton Step (<math>\approx</math> 26 min in).
+
-
: Self Concordance (<math>\approx</math> 35 min in).
+
-
: Equality Constrained Minimization (<math>\approx</math> 43 min in).
+
-
: Barrier function (<math>\approx</math> 47 min in).
+
-
: Central path (<math>\approx</math> 53 min in).
+
-
: Complexity analysis (<math>\approx</math> 1:14).
+
-
: Matlab for log-barrier (<math>\approx</math> 1:25).
+
-
: Primal-dual interior point methods (<math>\approx</math> 1:29).
+
-
 
+
-
[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).
+
-
: Reweighted TV for MRI 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).
+
-
: Geometric separation: Cartoon + Texture (<math>\approx</math> 22 min in).
+
-
: L1 synthesis ''vs''. analysis for CS (<math>\approx</math> 28 min in).
+
-
: Pulse reconstruction using L1 synthesis, L1 analysis and reweighted L1 analysis(<math>\approx</math> 36 min).
+
-
: ADC: nonuniform sampler ''vs''. random pre-integrator (<math>\approx</math> 48 min).
+
-
: Universal encoder (<math>\approx</math> 1:16 min).
+
-
 
+
-
[http://www.ima.umn.edu/recordings/New_Directions_Short_Course/ND6.4-15.07/discussion6-6-07.ram June 6, 2007] &nbsp;'''Discussion Session'''
+
-
 
+
== Introduction to Magnetic Resonance Imaging (MRI) ==
== Introduction to Magnetic Resonance Imaging (MRI) ==

Revision as of 13:29, 14 October 2008

RuFZx1 gks72nf95mdHfLav1Xpu

Contents

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

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

Personal tools