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L1 regularization (Lasso) adds a penalty \(\lambda \sum_{i=1}^{p} |w_i|\) to the loss, which pushes many coefficients exactly to zero and performs feature selection.
A set is convex if every line segment between any two of its points lies entirely inside the set.