Retractions: second order

Related lectures (32)

Page 2 of 4

Explores Taylor expansions of first order in optimization on manifolds.

Covers manifolds, topology, smooth maps, and tangent vectors in detail.

Covers smooth maps, vector fields, and retractions on manifolds, emphasizing the importance of smoothly varying curves.

Introduces Manopt, a toolbox for optimization on manifolds, focusing on solving optimization problems on smooth manifolds using the Matlab version.

Explores Riemannian metrics on manifolds and the concept of Riemannian submanifolds in Euclidean spaces.

Explores Riemannian connections on manifolds, emphasizing smoothness and compatibility with the metric.

Explores gradient descent methods for optimizing functions on manifolds, emphasizing small gradient guarantees and global convergence.

Explores upgrading foundations from embedded to general manifolds in optimization, discussing smooth sets and tangent vectors.

Explores smooth functions on manifolds, emphasizing continuity and atlas topologies.

Explores gradient descent with memory, momentum methods, conjugate gradients, and nonlinear CG on manifolds.