Lecture

Spectral Decomposition and SVD

In course

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Description

This lecture covers the spectral decomposition of symmetric matrices, including properties such as real eigenvalues, orthogonal eigenvectors, and diagonalizability. It also explores the Singular Value Decomposition (SVD) and its application in decomposing matrices into a sum of three matrices. The lecture demonstrates the calculation of eigenvalues, eigenvectors, and the importance of orthonormal bases in the context of matrix decomposition.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_6sytanx6

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Ontological neighbourhood

Mathematics

Algebra: Linear algebra

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