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Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
Published on Jan 25, 20124256 Views
Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic optimization problems which arise in machine learning. For strongly convex problems, its convergence rate was kno
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Chapter list
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization00:00
Stochastic Convex Optimization 00:02
Strongly Convex Stochastic Optimization - 0100:46
Strongly Convex Stochastic Optimization - 0201:08
Better Algorithms01:57
Related Work02:54
This Work - 0103:44
This Work - 0203:58
This Work - 0304:42
This Work - 0405:06
This Work - 0505:23
This Work - 0605:29
Smooth F - 0105:51
Smooth F - 0206:10
Smooth F - 0306:38
Non-Smooth F07:51
Warm-up08:25
Second Example09:52
Fixing SGD - 0110:54
Fixing SGD - 0211:25
Experiments - 0112:20
Experiments - 0214:25
Conclusions and Open Problems15:00