Newton's method on Riemannian manifolds

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Description

This lecture covers Newton's method on Riemannian manifolds, focusing on second-order optimality conditions, quadratic convergence, and solving the Newton system using a tangent space and Conjugate Gradient algorithm. The instructor explains retraction properties, intrinsic acceleration, and necessary and sufficient optimality conditions for smooth functions.

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Official source

Ontological neighbourhood

Mathematics

Geometry: Differential geometry

Related lectures (32)

https://mediaspace.epfl.ch/media/0_hcfnnezt

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In course

DEMO: consectetur dolore

Instructor

nulla reprehenderit do anim

Ontological neighbourhood

Mathematics

Geometry: Differential geometry

Related lectures (32)

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Differential Forms on Manifolds

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Hands on with Manopt: Optimization on Manifolds

Introduces Manopt, a toolbox for optimization on smooth manifolds with a Riemannian structure, covering cost functions, different types of manifolds, and optimization principles.