Lecture

Matrix Similarity and Diagonalization

In course

Eiusmod pariatur anim id consectetur id nisi mollit cillum eu. Elit aliquip deserunt ipsum cillum exercitation. Proident dolor in esse pariatur eiusmod voluptate do velit ad nostrud cillum ut cillum nisi. Ullamco nostrud duis deserunt deserunt. Occaecat incididunt consequat eiusmod ex nostrud esse et. Ullamco velit exercitation eu qui ullamco velit magna culpa labore. Reprehenderit est ipsum ut laboris nostrud in ex fugiat consequat.

Description

This lecture covers the concept of matrix similarity, eigenvalues, algebraic multiplicities, and diagonalization. It explains how to determine if two matrices are similar and when a matrix is diagonalizable. The instructor demonstrates the process of finding a special base that simplifies the matrix representation and explores the conditions for a matrix to be diagonalizable.

Instructor

Non sit veniam labore voluptate nulla eiusmod minim cillum non ex ut eiusmod nulla fugiat. Incididunt cillum sunt nostrud adipisicing ipsum officia deserunt. Veniam officia commodo adipisicing do excepteur irure id nulla. Deserunt ea et minim aliquip aliquip nisi est enim ex ad quis non est.

Official source

https://mediaspace.epfl.ch/media/0_5gjx3bth

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Related lectures (43)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.