systems of linear equations. That is, in linear algebra, there is interest in the relationship between the solutions of (1) and the row space, column space, and null space of A. In particular, finding the basis of the row space is related to solving linear systems. Specifically, the dimension of the row space and column space is called the rank, and the dimension of the null space is called the nullity.
Note that the column space is also called the image. If one considers the matrix A as a concept of a function, then the function corresponding to A∈Rm×n can also be seen as TA:Rn→Rm.
Theorem 1
A necessary and sufficient condition for the linear system Ax=b to have a solution is b∈C(A).
Theorem 2
Let x0 be some solution to Ax=b. Let us call S={v1,v2,…,vk} a basis for N(A). Then, all solutions of Ax=b can be expressed as follows.
x=x0+c1v1+c2v2+⋯+ckvk
Conversely, for all constants c1,c2,…,ck, the above x is a solution to Ax=b.
(2) is called the general solution of Ax=b. x0 is called the particular solution of Ax=b. Furthermore, c1v1+c2v2+⋯+ckvk is called the general solution of Ax=0.
From these theorems, it can be understood that the general solution of a nonhomogeneous linear system can be represented as the sum of a particular solution and the general solution of the homogeneous linear system.
The null space is written as kerA and also called the kernel. This is because it is a specialization of the general concept of kernel discussed in abstract algebra, seeing A as a function.
Howard Anton, Chris Rorres, Anton Kaul, Elementary Linear Algebra: Applications Version(12th Edition). 2019, p263-267 ↩︎