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A subgradient method with constant step-size for ℓ
1
-composite optimization
A. Scagliotti
, P. Colli Franzone
Chair of Applied Numerical Analysis
Munich Center for Machine Learning
University of Pavia
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1
-composite optimization'. Together they form a unique fingerprint.
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Keyphrases
Composite Optimization
100%
Subgradient Method
100%
Constant Step Size
100%
Linear Convergence
66%
Strongly Convex
66%
Value Function
33%
1 Regularization
33%
Slow Convergence
33%
Gradient Descent
33%
Convergence Results
33%
Convex Optimization Problem
33%
Nonsmooth
33%
Subdifferential
33%
Restart Strategy
33%
Non-differentiability
33%
Non-strongly Convex
33%
Adaptive Restart
33%
Proper Action
33%
Inertial Dynamics
33%
Mathematics
Step Size
100%
Subgradient
100%
Linear Convergence
66%
Convergence Rate
66%
Function Value
33%
Regularization
33%
Differentiability
33%
Convergence Result
33%
Subdifferential
33%
Convex Case
33%
Proper Action
33%
Smooth Term
33%