Rank of a Matrix

Definition

The rank of a matrix is a fundamental concept in linear algebra that represents the dimension of the vector space generated by the rows or columns of the matrix. It is a measure of the linear independence and the number of linearly independent rows or columns in the matrix.

5 Must Know Facts For Your Next Test

1. The rank of a matrix is always less than or equal to the minimum of the number of rows and the number of columns in the matrix.
2. The rank of a matrix is equal to the number of linearly independent rows or columns in the matrix.
3. The rank of a matrix is also equal to the dimension of the column space or the row space of the matrix.
4. The rank of a matrix is related to the number of solutions to a system of linear equations represented by the matrix. If the rank of the matrix is less than the number of variables, then the system has infinitely many solutions.
5. The rank of a matrix is an important concept in solving systems of equations using determinants, as it determines the number of linearly independent equations and the existence of a unique solution.

Review Questions

• Explain how the rank of a matrix is related to the number of linearly independent rows or columns in the matrix.
  • The rank of a matrix is equal to the number of linearly independent rows or columns in the matrix. This means that the rank represents the dimension of the vector space generated by the rows or columns of the matrix. If a matrix has $m$ rows and $n$ columns, its rank will be the maximum number of linearly independent rows or columns, which is always less than or equal to the minimum of $m$ and $n$.
• Describe the relationship between the rank of a matrix and the number of solutions to a system of linear equations represented by that matrix.
  • The rank of a matrix is closely related to the number of solutions to a system of linear equations represented by that matrix. If the rank of the matrix is less than the number of variables in the system, then the system has infinitely many solutions. This is because the null space of the matrix, which represents the set of all vectors that are mapped to the zero vector, has a dimension greater than zero. On the other hand, if the rank of the matrix is equal to the number of variables, then the system has a unique solution, as the null space of the matrix is the zero vector.
• Explain how the rank of a matrix is used in the context of solving systems of equations using determinants.
  • The rank of a matrix is a crucial concept in solving systems of equations using determinants. The rank of a matrix determines the number of linearly independent equations in the system, which is necessary for determining the existence and uniqueness of a solution. If the rank of the coefficient matrix is less than the number of variables, then the system has infinitely many solutions, and the determinant of the coefficient matrix will be zero. Conversely, if the rank of the coefficient matrix is equal to the number of variables, then the system has a unique solution, and the determinant of the coefficient matrix will be non-zero.

Related terms

Determinant:

The determinant of a square matrix is a scalar value that provides information about the matrix, such as whether it is invertible and the number of linearly independent rows or columns.

Null Space: The null space of a matrix is the set of all vectors $\vec{x}$ such that $A\vec{x} = \vec{0}$, where $A$ is the matrix. It represents the space of all vectors that are mapped to the zero vector by the matrix.

Row Echelon Form:

The row echelon form of a matrix is a transformed version of the matrix where the leading entry in each non-zero row is a 1, and all entries below it are 0. The number of non-zero rows in the row echelon form is equal to the rank of the matrix.