What does Singular value represent in the context of Singular Value Decomposition

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Regarding SVD: Why is it called Singular Value and what does that value represent? And where does the SVD play a part in applications? Thank you

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Judging by Why the SVD is named so... it sounds like the terminology is mostly caused by historical baggage rather than it really having much mathematical significance.

Anyway, most applications are based on the property that $\sigma_n=\max_{\| x \|=1,x \perp v_1,\dots,v_{n-1}} \| Ax \|$. The right singular vectors $v_n$ are the corresponding maximizers (which aren't unique), while $Av_n=\sigma_n u_n$. Thus you find the direction in the domain that $A$ makes the biggest, then you search among vectors orthogonal to that for the direction in the domain that $A$ makes biggest, and so on. You can show from this definition that the $u_n$ are also mutually orthogonal, thus this provides an orthogonal coordinate system for the domain and in general a different orthogonal coordinate system for the codomain such that in this pair of systems $A$ is diagonal with nonnegative entries.