Diagonal Matrix
From the book
Chapter 2: Matrix. In the chapter mind map this icon labels Diagonal. The discussion below is excerpted and lightly edited from § Definition: Diagonal Matrix in Mathematics for AI and Machine Learning. Related material also appears in Chapter 5 (Trace & Cyclic Props), Chapter 5 (Determinants & Inverses via LU).
That is, all off-diagonal entries are zero. A diagonal matrix has the form:
Notation: We often write $\text{diag}(d_0, d_1, \ldots, d_{N-1})$ to denote a diagonal matrix with diagonal entries $d_0, d_1, \ldots, d_{N-1}$.
What this drawing shows
What you see. Square matrix whose nonzero structure lies on the main diagonal, representing independent coordinate scaling.
In the mind map. Chapter 2 — Diagonal. See From the book above for definitions, figures, and worked examples.
Also appears in Ch. 5 (Trace & Cyclic Props); Ch. 5 (Determinants & Inverses via LU).
Where to read next
Read the full definitions, figures, and worked examples in Chapter 2: Matrix — see the mind-map node Diagonal.
This concept is also referenced in Chapter 5 (Trace & Cyclic Props); Chapter 5 (Determinants & Inverses via LU).