Invertible Matrix

What is an Invertible Matrix?

An invertible matrix, also known as a non-singular or nondegenerate matrix, is a square matrix that possesses an inverse. A square matrix is a matrix with the same number of rows and columns. The inverse of a matrix A is denoted as A-1

, and it is unique to A. The product of a matrix and its inverse is the identity matrix, which is a special type of diagonal matrix where all the elements on the main diagonal are ones and all other elements are zeros.

Properties of an Invertible Matrix

An invertible matrix has several important properties:

  • The determinant of an invertible matrix is non-zero. Conversely, if the determinant of a square matrix is zero, the matrix is singular and does not have an inverse.
  • The rows and columns of an invertible matrix are linearly independent. This means that no row or column can be written as a linear combination of the others.
  • For a matrix to be invertible, it must be square. Rectangular matrices (matrices with different numbers of rows and columns) do not have inverses.
  • The inverse of a matrix product is the product of the inverses in reverse order. In other words, if A and B are invertible matrices, then (AB)-1 = B-1A-1.
  • The transpose of an invertible matrix is also invertible, and the inverse of the transpose is the transpose of the inverse. That is, (AT)-1 = (A-1)T.

Calculating the Inverse of a Matrix

There are multiple methods to calculate the inverse of a matrix:

  • Algebraic Method: For a 2x2 matrix, the inverse can be calculated using a formula that involves swapping elements and changing the signs of some elements, followed by dividing by the determinant.
  • Gaussian Elimination:

    This method transforms the matrix into its reduced row echelon form using a series of row operations, and can be applied to matrices of any size.

  • Adjoint Method: This method involves calculating the cofactor matrix and the adjugate matrix, and then dividing by the determinant.

Applications of Invertible Matrices

Invertible matrices are widely used in various fields of mathematics and applied sciences:

  • Linear Algebra: Invertible matrices are crucial for solving systems of linear equations. If the coefficient matrix is invertible, the system has a unique solution.
  • Computer Graphics: In graphics, invertible matrices are used to perform various transformations such as rotation, scaling, and translation of objects.
  • Control Theory: In control systems, the controllability and observability matrices must be invertible for the system to be controllable and observable.
  • Cryptography: Some encryption techniques, such as the Hill cipher, use invertible matrices to encode and decode messages.

Non-Invertible Matrices

Matrices that are not invertible are referred to as singular or degenerate. These matrices do not have an inverse because they do not satisfy the conditions required for invertibility, such as having a non-zero determinant or linearly independent rows and columns. In practical terms, a singular matrix represents a transformation that reduces the dimensionality of the space, which cannot be reversed.


Invertible matrices play a fundamental role in linear algebra and its applications. Understanding the concept of matrix invertibility is essential for solving linear systems, performing matrix decompositions, and applying various mathematical transformations. While the computation of a matrix inverse can be complex, especially for large matrices, the concept of invertibility and its properties provide powerful tools for theoretical analysis and practical problem-solving in many scientific and engineering disciplines.

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