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Proximal gradient methods for learning

Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies algorithms for a general class of convex regularization problems where the regularization penalty may not be differentiable. One such example is ℓ 1 {\displaystyle \ell _{1}} regularization (also known…

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Lasso regularization

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Proximal gradient methods for learning

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displaystyle convex regularization lasso operator problem proximity group gradient methods penalty ell proximal function learning problems theory fixed point norm

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