Post

Conversation

リニア・テック 別府 伸耕
@linear_tec
行列の計算において「対角行列」は一番わかりやすい．そのため，各種の「変形」で中心的な役割を果たします． ここで解説している「特異値分解」を使えば，任意の行列を対角行列に変形できます．これがとにかく便利．この形式は様々な分野で登場します． linear-tec.jp/products/math/
The image is a detailed mathematical explanation of singular value decomposition (SVD) in the context of linear algebra. It includes diagrams and equations that illustrate how an arbitrary matrix \( A \) can be transformed into a diagonal matrix through SVD. The text is in Japanese and explains the significance of diagonal matrices in matrix calculations, emphasizing their clarity and central role in various transformations. The post text highlights the utility of SVD in converting any matrix into a diagonal form, which is widely applicable across different fields. The image is educational, aimed at those studying or interested in advanced mathematics, particularly linear algebra.
7,162
Views