## a square matrix has an inverse iff

### a square matrix has an inverse iff its determinant is non-zero

a square matrix has an inverse iff its determinant is non-zero.

To understand this statement, let’s define some terms and concepts first:

Square Matrix: A square matrix is a matrix that has the same number of rows and columns.

Inverse: The inverse of a matrix is denoted as A^(-1) and is a matrix that, when multiplied by the original matrix A, gives the identity matrix I. In other words, if A is a square matrix, then A * A^(-1) = I.

Determinant: The determinant of a square matrix is a scalar value that is calculated using a specific formula depending on the size of the matrix. It provides important information about the properties of the matrix.

Now, let’s discuss why a square matrix has an inverse iff its determinant is non-zero:

1. Non-zero Determinant: If the determinant of a square matrix is non-zero, it means that the matrix is invertible. In other words, there exists an inverse matrix A^(-1) that satisfies the equation A * A^(-1) = I. This is true because the non-zero determinant guarantees that the matrix can be transformed into the identity matrix using row operations.

2. Zero Determinant: If the determinant of a square matrix is zero, it means that the matrix is singular or non-invertible. In this case, there is no inverse matrix that can be multiplied with the original matrix to obtain the identity matrix. The presence of zero in the determinant formula indicates that the rows or columns of the matrix are linearly dependent, making it impossible to reverse the operations and find a unique inverse.

Therefore, a square matrix has an inverse iff its determinant is non-zero. If the determinant is zero, the matrix does not have an inverse, and if the determinant is non-zero, the matrix is invertible and has an inverse.

##### More Answers:

Understanding the Function f(x) = |x| + 2 | Step by Step Explanation and Graph VisualizationThe Dot Product of Matrices | Understanding and Calculation Explained

Understanding Matrix Multiplication | Commutative, Associative, and Distributive Properties