Journal article
TRACKING AND REGRET BOUNDS FOR ONLINE ZEROTH-ORDER EUCLIDEAN AND RIEMANNIAN OPTIMIZATION
AI Maass, C Manzie, D Nešić, JH Manton, I Shames
SIAM Journal on Optimization | Published : 2022
DOI: 10.1137/21M1405551
Abstract
We study numerical optimization algorithms that use zeroth-order information to minimize time-varying geodesically convex cost functions on Riemannian manifolds. In the Euclidean setting, zeroth-order algorithms have received a lot of attention in both the time-varying and time-invariant cases. However, the extension to Riemannian manifolds is much less developed. We focus on Hadamard manifolds, which are a special class of Riemannian manifolds with global nonpositive curvature that offer convenient grounds for the generalization of convexity notions. Specifically, we derive bounds on the expected instantaneous tracking error, and we provide algorithm parameter values that minimize the algor..
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Grants
Awarded by Multidisciplinary University Research Initiative
Funding Acknowledgements
This work is supported by the Australian Research Council (DP210102454) and the Australian Government via grant AUSMURIB000001, associated with ONR MURI grant N00014-19-1-2571.