On the connections between algorithmic regularization and penalization for convex losses
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Abstract (arXiv)
In this work we establish the equivalence of algorithmic regularization and explicit convex penalization for generic convex losses. We introduce a geometric condition for the optimization path of a convex function, and show that if such a condition is satisfied, the optimization path of an iterative algorithm on the unregularized optimization problem can be represented as the solution path of a corresponding penalized problem.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2019.
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