Inverse Matrix Calculator

Calculate matrix inverse using Gauss-Jordan elimination with step-by-step solutions

Matrix Input

Only square matrices can have inverses

Enter Matrix Elements

Matrix Inverse Results

Enter values in the matrix to calculate its inverse

The matrix must be square and non-singular (determinant ≠ 0)

Example: 2×2 Matrix Inverse

Input Matrix

A = [2, 1]
     [3, 4]

Solution Steps

Step 1: Calculate determinant: det(A) = (2)(4) - (1)(3) = 8 - 3 = 5

Step 2: Since det(A) ≠ 0, matrix is invertible

Step 3: For 2×2 matrix: A⁻¹ = (1/det(A)) × [d, -b; -c, a]

Step 4: A⁻¹ = (1/5) × [4, -1; -3, 2]

A⁻¹ = [0.8, -0.2]
       [-0.6, 0.4]

Matrix Inverse Properties

1

Square Matrix

Only square matrices can have inverses

Same number of rows and columns

2

Non-singular

Determinant must be non-zero

If det(A) = 0, no inverse exists

3

Identity Property

A × A⁻¹ = I

Multiplication gives identity matrix

Key Formulas

2×2 Matrix Inverse

A⁻¹ = (1/det(A)) × [d, -b; -c, a]

Determinant (2×2)

det(A) = ad - bc

General Formula

A⁻¹ = (1/det(A)) × adj(A)

Identity Check

A × A⁻¹ = A⁻¹ × A = I

Understanding Matrix Inverse

What is a Matrix Inverse?

The inverse of a matrix A is another matrix A⁻¹ such that when multiplied together, they produce the identity matrix. It's analogous to finding the reciprocal of a number, where a × (1/a) = 1. For matrices, A × A⁻¹ = I (identity matrix).

Requirements for Inverse

  • Square matrix: Must have equal rows and columns
  • Non-singular: Determinant must be non-zero
  • Full rank: All rows/columns must be linearly independent

Applications

  • Solving systems of linear equations
  • Computer graphics transformations
  • Cryptography and data encoding
  • Engineering and physics calculations
  • Machine learning and statistics

Note: When a matrix doesn't have an inverse (singular matrix), you can use the pseudoinverse for approximate solutions in applications like least squares regression.

Calculation Methods

Gauss-Jordan Elimination:

  • • Create augmented matrix [A | I]
  • • Use row operations to get [I | A⁻¹]
  • • More numerically stable
  • • Works for any size matrix

Adjugate Method:

  • • Calculate matrix of cofactors
  • • Transpose to get adjugate
  • • Divide by determinant
  • • Formula: A⁻¹ = adj(A)/det(A)