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- Usage of the word orthogonal outside of mathematics
In debate(?), "orthogonal" to mean "not relevant" or "unrelated" also comes from the above meaning If issues X and Y are "orthogonal", then X has no bearing on Y If you think of X and Y as vectors, then X has no component in the direction of Y: in other words, it is orthogonal in the mathematical sense
- linear algebra - What is the difference between orthogonal and . . .
Two vectors are orthogonal if their inner product is zero In other words $\langle u,v\rangle =0$ They are orthonormal if they are orthogonal, and additionally each vector has norm $1$ In other words $\langle u,v \rangle =0$ and $\langle u,u\rangle = \langle v,v\rangle =1$ Example For vectors in $\mathbb{R}^3$ let
- orthogonal vs orthonormal matrices - what are simplest possible . . .
Generally, those matrices that are both orthogonal and have determinant $1$ are referred to as special orthogonal matrices or rotation matrices If I read "orthonormal matrix" somewhere, I would assume it meant the same thing as orthogonal matrix Some examples: $\begin{pmatrix} 1 1 \\ 0 1 \end{pmatrix}$ is not orthogonal
- How do we know that nullspace and row space of a matrix are orthogonal . . .
How do we know that not only are these two subspaces orthogonal, they are also orthogonal complements because there is no vector in $\mathbb{R}^n$ that is perpendicular to the vectors in the nullspace that is not in the row space, and there is no vector perpendicular to the vectors in the row space that is not in the null space?
- What is orthogonal transformation? - Mathematics Stack Exchange
Matrices represents linear transformation (when a basis is given) Orthogonal matrices represent transformations that preserves length of vectors and all angles between vectors, and all transformations that preserve length and angles are orthogonal Examples are rotations (about the origin) and reflections in some subspace
- Eigenvectors of real symmetric matrices are orthogonal
Now find an orthonormal basis for each eigenspace; since the eigenspaces are mutually orthogonal, these vectors together give an orthonormal subset of $\mathbb{R}^n$ Finally, since symmetric matrices are diagonalizable, this set will be a basis (just count dimensions)
- How do you orthogonally diagonalize the matrix?
$\begingroup$ The same way you orthogonally diagonalize any symmetric matrix: you find the eigenvalues, you find an orthonormal basis for each eigenspace, you use the vectors in the orthogonal bases as columns in the diagonalizing matrix $\endgroup$ –
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