Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets

• URL: http://arxiv.org/abs/2303.05037v3
• Date: Sat, 20 Jul 2024 22:09:10 GMT
• Title: Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets
• Authors: Ning Liu, Benjamin Grimmer,
• Abstract summary: We consider feasibility and constrained optimization problems defined over smooth and/or strongly convex sets. We propose new scalable, projection-free, accelerated first-order methods in these settings.
• Score: 4.5344287283782405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider feasibility and constrained optimization problems defined over smooth and/or strongly convex sets. These notions mirror their popular function counterparts but are much less explored in the first-order optimization literature. We propose new scalable, projection-free, accelerated first-order methods in these settings. Our methods avoid linear optimization or projection oracles, only using cheap one-dimensional linesearches and normal vector computations. Despite this, we derive optimal accelerated convergence guarantees of $O(1/T)$ for strongly convex problems, $O(1/T^2)$ for smooth problems, and accelerated linear convergence given both. Our algorithms and analysis are based on novel characterizations of the Minkowski gauge of smooth and/or strongly convex sets, which may be of independent interest: although the gauge is neither smooth nor strongly convex, we show the gauge squared inherits any structure present in the set.

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