This lecture covers the concept of l2-embeddability theorem, discussing isometric embeddings, and providing examples and proofs related to positive semidefinite matrices and eigenvalues computation.
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The course aims to introduce the basic concepts and results on metric embeddings, or more precisely on approximate embeddings. This area has been under rapid development since the 90's and it has stro
Provides a review of linear algebra concepts crucial for convex optimization, covering topics such as vector norms, eigenvalues, and positive semidefinite matrices.
