Originally posted by: ZeroNine8
I was using this definition of orthogonal:
N mutually orthogonal vectors span an N-dimensional
vector space, meaning that, any vector in the space can be
expressed as a linear combination of the vectors. This is
true of any set of N linearly independent vectors.
The term is used loosely to mean mutually independent or well
separated. It is used to describe sets of primitives or
capabilities that, like linearly independent vectors in
geometry, span the entire "capability space" and are in some
sense non-overlapping or mutually independent. For example,
in logic, the set of operators "not" and "or" is described as
orthogonal, but the set "nand", "or", and "not" is not
(because any one of these can be expressed in terms of the
others).
which basically means each coordinate is linearly independent of the others.
If you read your definition correctly you can see that orthogonal is a special case of linearly independent. The bold portion of your definition refers to the fact that what was said of orthogonal vectors is also true of linearly independent vectors. Any orthogonal set of vectors is linearly independent but not all sets of linearly Independent vectors are orthogonal. Linearly independent is the more general case, to from a basis for a space a set of vectors MUST be linearly Independent, but not necessarily orthogonal.
Vectors are linearly dependent or independent not coordinates. I take coordinate to mean a set of numbers which specify a location in the space. You can associate a vector with a coordinate if you assume the origin forms one end of the vector.