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Dynamic ℓ1 Reconstruction
Published on Aug 26, 20134509 Views
Sparse signal recovery often involves solving an ℓ1-regularized optimization problem. Most of the existing algorithms focus on the static settings, where the goal is to recover a fixed signal from a
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Chapter list
Dynamic l1 Reconstruction00:00
Underdetermined systems of equations01:13
Sparse recovery03:15
Random matrices (1)05:20
Random matrices (2)06:52
Random matrices (3)08:21
Random matrices (4)08:36
Sparsity09:33
Wavelet app.10:01
Integrating compression and sensing10:21
Goal13:04
Agenda15:53
Classical (1)16:25
Classical (2)17:01
Classical (3)19:34
Classical (4)21:37
Dynamic sparse recovery21:42
Optimality23:31
Example (1)25:38
Example (2)27:02
Path29:00
Sparse innovations29:47
Numerical experiments (1)31:10
Adding a measurement (1)31:59
Adding a measurement (2)32:45
Numerical experiments (2)33:04
Reweighted33:34
Changing weights34:58
Numerical experiments (3)35:19
Framework (1)36:16
Framework (2)37:53
Solution38:43
Streaming measurements39:38
Streaming basis40:41
Streaming sparse (1)41:21
Streaming sparse (2)42:16
Streaming signal recovery (1)42:34
Streaming signal recovery (2)43:14
Dynamic signal (1)43:42
Dynamic signal (2)43:58
Dynamical systems for sparse recovery (1)44:22
App. analog computing44:26
Analog vector47:23
Dynamical systems for sparse recovery (2)47:54
Locally competitive algorithm48:25
Key questions (1)49:16
Key questions (2)49:59
LCA convergence (1)50:22
LCA convergence (2)50:28
LCA convergence (3)50:35
LCA convergence (4)50:36
Convergence (1)51:13
Convergence (2)51:19
Activation51:45
Efficient activation52:22
References53:04