This lecture explores the importance of comparing tangent vectors at different points, focusing on algorithms like RCG and BFGS, finite differences in R², and Lipschitz continuous gradients.
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Learn to optimize on smooth, nonlinear spaces: Join us to build your foundations (starting at "what is a manifold?") and confidently implement your first algorithm (Riemannian gradient descent).
Explores transporters as a practical alternative to parallel transport, discussing minimal requirements, examples with matrices, pragmatic choices, and optimization algorithms.