Riemannian manifold

Related lectures (32)

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Covers the convergence theorem of Riemannian Gradient Descent and the line search method.

Explores matrix-based approaches for computing the Newton step on a Riemannian manifold.

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

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

Delves into Riemannian geometry for robot motion learning and control, emphasizing geodesic synergies and the configuration space manifold.

Covers optimization on manifolds, focusing on smooth manifolds and functions, and the process of gradient descent.

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

Explores practical aspects of Riemannian trust-region optimization and introduces the truncated conjugate gradient method.

Covers Riemannian metrics, gradients, vector fields, and inner products on manifolds.

Introduces the Riemannian Trust Regions (RTR) framework, covering conjugate directions, Newton's method, and model improvement.