List of various vector (linear) spaces [closed]

The vector (linear) space is defined as a non-empty set L over a field F, where two relations (binary operations) are defined:

$\oplus: L \times L \longrightarrow L$

2. Scalar multiplication

$\odot: F \times L \longrightarrow L$

Although we call these relations as addition and scalar multiplication, both of these relations can have arbitrary forms, which do not need to have anything in common with the traditional apprehension of the addition and the multiplication (for example the addition and the multiplication of real numbers). From the set-theoretical point of view are both operations just mappings between two sets, closed under the relations, which need to meet following conditions (axioms of linear space):

1. Associativity of addition
2. Commutativity of addition
3. Identity element of addition
4. Inverse elements of addition
5. Distributivity of scalar multiplication with respect to vector addition
6. Distributivity of scalar multiplication with respect to field addition
7. Compatibility of scalar multiplication with field multiplication
8. Identity element of scalar multiplication

We are usually working with the linear space of complex numbers $\mathbb{C}^n$, because all linear spaces of dimension of $n$ are isomorphic. In other words, we can use linear transformation between two different linear spaces and equivalently solve the problem in some well known linear space (usually $\mathbb{C}^n$) and then transform it back.

My question is: What kinds of linear spaces do you know? What fields and binary operations compose the linear space. If you can, please, also note the physical application of such a linear space.

I will just summarize the ones I know:

1. Vector space $\mathbb{C}^n$, field of complex numbers $\mathbb{C}$

Let $\mathbf{x},\mathbf{y} \in \mathbb{C}^n$ and $\alpha \in \mathbb{C}$, where $\mathbf{x} = (x_1, x_2, ..., x_n)$ and $\mathbf{y} = (y_1, y_2, ..., y_n)$. Operation of addition $\oplus$ and scalar multiplication $\odot$ are defined as

$\mathbf{x} \oplus \mathbf{y} \triangleq (x_1 + y_1, x_2 + y_2, ..., x_n + y_n)$

$\alpha \odot \mathbf{x} \triangleq (\alpha \cdot x_1, \alpha \cdot x_2, ..., \alpha \cdot x_n)$

(Note: Plus symbol and dot symbol in the brackets denote operations of addition and multiplication of complex numbers.)

2. Function space, field of complex numbers $\mathbb{C}$

$f \oplus g \triangleq f(x) + g(x)$

$\alpha \odot f \triangleq \alpha f(x)$

3. Vector space of possitive real numbers $\mathbb{R}^+$, field of real numbers $\mathbb{R}$

$\mathbf{x} \oplus \mathbf{y} \triangleq x \cdot y$

$\alpha \odot \mathbf{x} \triangleq x^{\alpha}$

4. Vector space of matrices $\mathbb{F}^{m \times n}$ over field $\mathbb{F}$

$(\mathbf{X} \oplus \mathbf{Y})_{i,j} \triangleq (\mathbf{X})_{i,j} + (\mathbf{Y})_{i,j}$

$(\alpha \odot \mathbf{X})_{i,j} \triangleq \alpha(\mathbf{X})_{i,j}$

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closed as too broad by Marc van Leeuwen, T. Bongers, Cameron Buie, Daniel Rust, Lord_FarinSep 20 '13 at 9:06

There are either too many possible answers, or good answers would be too long for this format. Please add details to narrow the answer set or to isolate an issue that can be answered in a few paragraphs. If this question can be reworded to fit the rules in the help center, please edit the question.

I think this question is a little too broad for this site. There is no possible way of making a list of all known vector spaces, and even if you had one I don't know what purpose it would serve. –  Rahul Feb 1 '13 at 2:05
For (3) you need positive real numbers, not just non-negative. –  Jim Feb 1 '13 at 2:12
I realize that there exist uncountable number of linear spaces, but I still wonder how distinct they can be. The motivation is to better recognize the linearity in some physical problems. Examples can help a lot.Thanks Jim - you are right. –  OukiDouki Feb 1 '13 at 2:30

Every field extension $E:F$ gives rise to $E$ as a vector space over $F$. The dimension of this vector spaces tells us a lot about the size of the extension and its behaviour.
In linear algebra itself, the vector space of all linear transformations from one vector space to another is a very important vector space. The vector space of all $m\times n$ matrices is of course closely related.