Constrained Optimization On Riemannian Manifolds
berkeley.edu
Constrained Optimization On Riemannian Manifolds
Many applications involve non-Euclidean data, where exploiting Riemannian geometry can deliver algorithms that are computationally superior to standard nonlinear programming approaches. This observation has resulted in an increasing interest in Riemannian methods in the optimization and machine learning community. In this talk, we consider the ...
Nov 29, 2021
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