Date
Tue, 23 Jan 2024
Time
14:30 - 15:00
Location
L6
Speaker
Boris Shustin
Organisation
Mathematical Institute (University of Oxford)

Optimization problems constrained to a smooth manifold can be solved via the framework of Riemannian optimization. To that end, a geometrical description of the constraining manifold, e.g., tangent spaces, retractions, and cost function gradients, is required. In this talk, we present a novel approach that allows performing approximate Riemannian optimization based on a manifold learning technique, in cases where only a noiseless sample set of the cost function and the manifold’s intrinsic dimension are available.

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