Performance Estimation for Smooth and Strongly Convex Sets
Published in ArXiv Preprint, 2024
We extend recent computer-assisted design and analysis techniques for first-order optimization over structured functions–known as performance estimation–to apply to structured sets. We prove “interpolation theorems” for smooth and strongly convex sets with Slater points and bounded diameter, showing a wide range of extremal questions amount to structured mathematical programs. Prior function interpolation theorems are recovered as a limit of our set interpolation theory. Our theory provides finite-dimensional formulations of performance estimation problems for algorithms utilizing separating hyperplane oracles, linear optimization oracles, and/or projection oracles of smooth/strongly convex sets. As direct applications of this computer-assisted machinery, we identify the minimax optimal separating hyperplane method and several areas for improvement in the theory of Frank-Wolfe, Alternating Projections, and non-Lipschitz Smooth Optimization. While particular applications and methods are not our primary focus, several simple theorems and numerically supported conjectures are provided.
Recommended citation: Alan Luner, Benjamin Grimmer. (2024). "Performance Estimation for Smooth and Strongly Convex Sets." ArXiv Preprint 2410.14811 .
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