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Xyius
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What is the distinct difference between the kernel and the null space? They both have the same definition, namely, Ax=0.
Fredrik said:There's no difference. Not in linear algebra (or functional analysis) anyway, but the word "kernel" is used for different things in other areas of mathematics, and "null space" isn't (as far as I know).
Fredrik said:There's no difference. Not in linear algebra (or functional analysis) anyway, but the word "kernel" is used for different things in other areas of mathematics, and "null space" isn't (as far as I know).
Fredrik said:So you're saying that "kernel" is used for linear operators, but not for the corresponding matrices, and that "null space" is used for matrices, but not for the corresponding linear operators? I don't think that terminology is standard.
Fredrik said:OK, that terminology might be common, I don't know. Axler isn't using it. Page 41 defines the null space of a linear operator.
Yes, that's precisely my point. You said that when [itex]T\in\mathcal L(V,W)[/itex], the set [itex]\{x\in V|Tx=0\}[/itex] is called the "kernel" of T, and not the "null space" of T. So this example shows that there's at least one author that doesn't use the same terminology as you. (There may be others who agree with you. My point is just that your terminology certainly isn't used by everyone).Newtime said:Yes but isn't the notation "L(V, W)" referring to the set of all linear operators from the vector space V to the vector space W?
I haven't seen the term "linear operator" used in any other context, but I suppose we could use it for functions between modules as well. (A module is essentially a vector space over a ring, instead of over a field). I don't see how the term could make sense in any other context.Newtime said:In general, linear operators need not take vector spaces to vector spaces,
Fredrik said:Yes, that's precisely my point. You said that when [itex]T\in\mathcal L(V,W)[/itex], the set [itex]\{x\in V|Tx=0\}[/itex] is called the "kernel" of T, and not the "null space" of T. So this example shows that there's at least one author that doesn't use the same terminology as you. (There may be others who agree with you. My point is just that your terminology certainly isn't used by everyone).
I haven't seen the term "linear operator" used in any other context, but I suppose we could use it for functions between modules as well. (A module is essentially a vector space over a ring, instead of over a field). I don't see how the term could make sense in any other context.
There are (at least) two generalizations of the notion of "kernel":Fredrik said:(A module is essentially a vector space over a ring, instead of over a field). I don't see how the term could make sense in any other context.
shelovesmath said:I'm bumping this question. I'm wondering if there is a difference as far as the kernel being a set and the null space being a subspace. Is the kernel actually a subspace itself of the vector space it is mapping from? Or is it only just a set of vectors that maps to 0 on another vector space?
As others have said, in Linear Algebra, "kernel" (of a linear transformation) and "nullspace" are the same thing. One can show that the kernel of any linear transformation, from one vector space to another, is subspace (perhaps trivial) of the domain space.shelovesmath said:I'm bumping this question. I'm wondering if there is a difference as far as the kernel being a set and the null space being a subspace. Is the kernel actually a subspace itself of the vector space it is mapping from? Or is it only just a set of vectors that maps to 0 on another vector space?
The kernel and nullspace are both mathematical concepts that are used in linear algebra. The main difference between the two is their definition and how they are used in relation to a matrix.
The kernel of a matrix is the set of all vectors that, when multiplied by the matrix, result in a zero vector. The nullspace, on the other hand, is the set of all solutions to the homogeneous system of equations represented by the matrix.
The nullspace is a subset of the kernel, meaning that all vectors in the nullspace are also in the kernel. However, the nullspace can also contain additional vectors that are not in the kernel.
The kernel and nullspace are important in understanding the properties of a matrix, such as invertibility and rank. They are also used in solving systems of linear equations and in applications such as data compression and image processing.
Yes, the concept of the kernel and nullspace can be applied to many real-world scenarios. For example, in image processing, the nullspace can be used to identify and remove background noise from a digital image. In data compression, the kernel can be used to reduce the dimensionality of a dataset while still preserving important information.