Gradient Descent

Related lectures (32)

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Explores Taylor expansions and retractions on Riemannian manifolds, emphasizing second-order approximations and covariant derivatives.

Introduces optimization on manifolds, covering classical and modern techniques in the field.

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

Explains Riemannian submanifolds, metrics, and gradients computation on manifolds.

Explores non-analytic smooth functions, their properties, and applications in differential geometry and partitioning unity.

Explores Taylor expansions of first order in optimization on manifolds.

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

Explores Newton's method for optimizing functions on manifolds using second-order information and discusses its drawbacks and fixes.

Explores necessary and sufficient optimality conditions for local minima on manifolds, focusing on second-order critical points.

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