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Approximating Concavely Parameterized Optimization Problems

Published on Jan 16, 20133970 Views

Sören Laue

We consider an abstract class of optimization problems that are parameterized concavely in a single parameter, and show that the solution path along the parameter can always be approximated with ac

NIPS Conference 2012 - Lake Tahoe

Related categories

Semi-supervised LearningSupervised LearningUnsupervised LearningConvex Optimization

Chapter list

Approximating Concavely Parameterized Optimization Problems00:00
Where is Jena?00:20
Where is Jena? (1)00:24
Introduction00:32
General Regularization Paths00:56
General Problem – Regularization Path03:05
Regularization Paths03:17
Abstract Problem04:20
Grid Search / General Idea (1)04:58
Grid Search / General Idea (2)05:05
Grid Search / General Idea (3)05:07
Grid Search / General Idea (4)05:10
Grid Search / General Idea (5)05:11
Grid Search / General Idea (6)05:13
Grid Search / General Idea (7)05:16
Grid Search / General Idea (8)05:45
Grid Search / General Idea (9)05:54
Grid Search / General Idea (10)05:56
Grid Search / General Idea (11)05:59
Grid Search / General Idea (12)06:09
Grid Search / General Idea (13)06:12
Grid Search / General Idea (14)06:13
Grid Search / General Idea (15)06:14
Grid Search / General Idea (16)06:15
Grid Search / General Idea (17)06:16
Grid Search / General Idea (18)06:17
Grid Search / General Idea (19)06:17
Grid Search / General Idea (20)06:19
Grid Search / General Idea (21)06:41
Adaptive Step Size (1)07:09
Adaptive Step Size (2)07:18
Adaptive Step Size (3)07:31
Adaptive Step Size – Constrained Form07:39
Path Following Algorithm (1)07:50
Path Following Algorithm (2)08:05
Path Following Algorithm (3)08:08
Path Following Algorithm (4)08:13
Path Following Algorithm (5)08:23
Path Following Algorithm (6)08:30
Path Following Algorithm (7)08:33
Path Following Algorithm (8)08:38
Path Following Algorithm (9)08:39
Path Following Algorithm (10)08:40
Path Following Algorithm (11)08:40
Path Following Algorithm (12)08:43
Path Following Algorithm (13)08:44
Path Following Algorithm (14)08:45
Path Following Algorithm (15)08:46
Path Following Algorithm (16)08:47
Path Following Algorithm (17)08:48
Path Following Algorithm (18)08:49
Path Following Algorithm (19)08:50
Path Following Algorithm (20)08:51
Path Following Algorithm (21)08:52
General Problem (1)08:59
Adaptive Step Size – Additive Form09:27
Path Following Algorithm (22)09:31
Path Following Algorithm (23)09:37
Path Following Algorithm (23)09:44
Path Following Algorithm (24)10:03
Path Following Algorithm (25)10:20
Path Following Algorithm (26)10:32
Path Following Algorithm (27)10:45
Path Following Algorithm (28)10:54
Path Following Algorithm (29)11:05
Path Following Algorithm (30)11:17
Path Following Algorithm (31)11:17
Path Following Algorithm (32)11:18
Path Following Algorithm (33)11:19
Path Following Algorithm (34)11:19
Path Following Algorithm (35)11:20
Path Following Algorithm (36)11:21
Path Following Algorithm (37)11:21
Path Following Algorithm (38)11:30
General Problem (2)11:56
Path Algorithm (39)12:34
Regularization Paths / Cross-validation (1)12:42
Regularization Paths / Cross-validation (3)12:47
Path Complexity13:18
Conclusion — Generalize and Simplify13:48