Full Rank Matrix: Determinant Condition | Rank-Nullity Theorem

In summary, the conversation discusses the relationship between the full rank of a matrix ##A## and the condition ##ad-bc \neq 0##, where ##A## is a ##2 \times 2## matrix with elements ##a, b, c, d##. The conversation also explores the concept of linear independence and dependence of column vectors in a matrix. The conversation concludes with a suggestion to show that ##ad-bc = 0## if and only if the column vectors ##(a,c)## and ##(b,d)## are linearly dependent.
  • #1
squenshl
479
4

Homework Statement


Show that the matrix ##A## is of full rank if and only if ##ad-bc \neq 0## where $$A = \begin{bmatrix}
a & b \\
b & c
\end{bmatrix}$$

Homework Equations

The Attempt at a Solution


Suppose that the matrix ##A## is of full rank. That is, rank ##2##. Then by the rank-nullity theorem, the
dimension of the kernel is ##0##. This implies that there exists an inverse ##A^{-1}## but this will only occur if ##ad-bc \neq 0## otherwise our matrix ##A## will be singular. On the other hand, suppose ##ad-bc \neq 0##. Hence, ##A## is nonsingular and there exists an inverse ##A^{-1}## but this will occur only when the dimension of the kernel is ##0##, that is, of rank ##n = 2##.
 
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  • #2
Your matrix has rank 2 iff the column vectors are linearly independent.
Can you show that the determinant of the matrix is zero iff the column vectors are linearly dependent ?
 
  • #3
Sorry $$A=\begin{bmatrix}
a & b \\
c & d
\end{bmatrix}$$ if that even matters!

How could I do this without using anything on determinants?
 
  • #4
Show that ad - bc = 0 iff ##\vec u = (a,c) ## and ##\vec v = (b,d)## are linearly dependent
 
  • #5
Cheers!
 

What is the definition of a "full rank" matrix?

A "full rank" matrix is a square matrix in which all of the rows and columns are linearly independent, meaning that no row or column can be expressed as a linear combination of the other rows or columns. In other words, a full rank matrix has a rank equal to its dimensions (number of rows or columns).

How do you determine the rank of a matrix?

The rank of a matrix can be determined by performing row reduction operations on the matrix until it is in reduced row echelon form. The number of non-zero rows in the reduced matrix is equal to the rank of the original matrix.

What is the significance of a "full rank" matrix?

A full rank matrix is significant because it is invertible, meaning that it has an inverse matrix that can be used to solve systems of linear equations. It also has a unique solution and can be used for various mathematical operations, such as calculating determinants and eigenvalues.

Can a matrix have a rank greater than its dimensions?

No, a matrix cannot have a rank greater than its dimensions. The rank of a matrix is always equal to the smaller of its number of rows or columns.

What are some real-world applications of "full rank" matrices?

Full rank matrices have many applications in fields such as engineering, physics, and computer science. They are used for solving systems of linear equations, image processing, data compression, and machine learning algorithms, among others.

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