I don't understand why the rank = n - Rank-nullity theorem - nullity

In summary, the rank-nullity theorem states that the rank of a linear transformation A, which is the dimension of its range, plus its nullity, which is the dimension of its null space, is equal to the dimension of its domain. This theorem can be used to prove that the rank of a linear transformation is equal to the number of linearly independent columns in its matrix representation. Additionally, the null space of a linear transformation is the set of all vectors that are mapped to the zero vector, and the nullity is the dimension of this space.
  • #1
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I don't understand why the rank = n -- Rank-nullity theorem -- nullity

Homework Statement


I'm working on #1 (the solutions are also included in that pdf) here ( http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/ax-b-and-the-four-subspaces/exam-1/MIT18_06SCF11_ex1s.pdf ).

Homework Equations


Ax = b
Ax = 0
rank A+ nullity A = n

The Attempt at a Solution


For #1(a), I don't understand why the rank = n. I've been told to look at the Rank-nullity theorem which states that rank A+ nullity A = n but, I don't understand what nullity means exactly. In fact, I'm not very solid on the meaning of rank either (but, I kind of get it).

Any input would be greatly appreciated!
 
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  • #2


A linear transformation, A:U->V, maps vectors in a vector space, U, to a vector space V. It is fairly straight forward to show that the "range" of A, {v| v= Au for some u in U} is a subspace of V. The 'rank' of A is the dimension of that subspace. It is similarly straight forward to show that the null space of A, {u| AU= 0}. The 'nullity' of A is the dimension of the null space. One way of approaching the proof of the "rank-nullity" theorem is to choose a basis for the null space, [itex]\{u_1, u_2, ..., u_m\}[/itex] and expand it to a basis for U by adding vectors [itex]\{u_{m+1}, ..., u_n\}[/itex]. Show that every vector in the range of A can be written as a linear combination of [itex]\{Au_{m+1}, ..., Au_n\}[/itex] and so has dimension n- m.
 
  • #3


Two hints:

The first statement,
$$Ax = \left[ \begin{array}{c} 1 \\ 1 \\ 1 \end{array} \right] \textrm{ has no solutions}$$
means that ##A## is not ...? (Injective, surjective?)

The second statement,
$$Ax = \left[ \begin{array}{c} 0 \\ 1 \\ 0 \end{array} \right] \textrm{ has exactly one solution}$$
means that the kernel (null space) of ##A## is ...?
 

Related to I don't understand why the rank = n - Rank-nullity theorem - nullity

What is the Rank-Nullity Theorem?

The Rank-Nullity Theorem is a fundamental theorem in linear algebra that states that the rank of a matrix plus the nullity of the matrix equals the number of columns of the matrix. In other words, it relates the dimensions of the column space and null space of a matrix.

Why is the Rank-Nullity Theorem important?

The Rank-Nullity Theorem is important because it allows us to determine the dimensions of the column space and null space of a matrix without explicitly calculating them. This can save time and effort in certain applications, such as solving systems of linear equations or finding eigenvalues and eigenvectors.

What is the relationship between rank and nullity?

The rank and nullity of a matrix are complementary, meaning that they add up to the total number of columns in the matrix. This means that if the rank of a matrix is n, then the nullity must be m-n, where m is the number of columns.

How is the Rank-Nullity Theorem used in real-world applications?

The Rank-Nullity Theorem has many applications in fields such as engineering, physics, and computer science. It can be used to solve systems of linear equations, analyze data in statistics, and understand the behavior of physical systems.

Can the Rank-Nullity Theorem be extended to other types of matrices?

Yes, the Rank-Nullity Theorem can be extended to square matrices, rectangular matrices, and even linear transformations between vector spaces. In each case, the theorem relates the dimensions of the range and null space of the matrix or transformation.

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