Vector space, the concept of linear algebra, the generalization of plane V2 and space V3 in analytic geometry. After determining the coordinate system, the points on the plane can be represented by real number pairs (a, b), and the points on the space can be represented by ternary real number arrays (a, b, c). To sum up, consider that the set of n-ary arrays Fn = {(a 1, ..., an) | ai ∈ f, I = 1, 2, ..., n} is in the number field f, and the algebraic system formed by Fn multiplication matrix is called the N-dimensional vector space or N-dimensional linear space on f, fn. Similar to any coordinate system in V3, each vector has a unique coordinate, and each vector A = (A 1, …, an) in Fn can be represented by E 1 = (1, 0, …, 0), and E2 = (0, 1, …,. E 1, …, en is called a base of Fn, n is called the dimension of Fn, and (a 1, …, an) is called the coordinates of a about the base e 1, …, en. The definition of vector space can also be generalized. If V is a non-empty set, V has addition, and the number field F has multiplication on V, and these two operations meet certain conditions, then V is called a vector space on F, and its elements are called vectors. If a 1, …, an, β∈V, l 1, …, ln∈F, β = l 1α 1+…+lnan, then β can be a 1. If every vector in V can be uniquely represented by a 1, …, an, then a 1, …, an is a base of V, n is the dimension of V, and every N-dimensional vector space on F has the same algebraic properties as Fn, that is, they are isomorphic. Vector space discusses the linear relationship among vectors, subspaces and spatial decomposition. When discussing linear problems in mathematics, we can use the viewpoint of vector space.

So you can have a lot of space.