Matrix Inverse Calculator

Calculate the inverse of 2x2 and 3x3 matrices with step-by-step solutions and determinant calculations

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A⁻¹ = (1/det A) × adj(A)

Calculate the inverse of square matrices using the adjugate method

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Quick Examples

Error

Determinant of Matrix A

Matrix is invertible ✓

Inverse Matrix A⁻¹

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Adjugate Matrix adj(A)

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Step-by-Step Solution

1

Calculate the determinant

det(A) =

2

Find the adjugate matrix

Swap diagonal elements, negate off-diagonal elements

Calculate cofactors and transpose the cofactor matrix

3

Calculate the inverse

A⁻¹ = (1/) × adj(A)

Verification

You can verify by multiplying A × A⁻¹ = I (identity matrix)

Matrix Inverse Formulas

Size Determinant Inverse Formula
2×2 ad - bc (1/det) × [[d,-b],[-c,a]]
3×3 Cofactor expansion (1/det) × adj(A)

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About Matrix Inverse Calculator

What is a Matrix Inverse?

The inverse of a matrix A (denoted A⁻¹) is a matrix that, when multiplied by A, yields the identity matrix:

A × A⁻¹ = A⁻¹ × A = I

Not all matrices have inverses. A matrix must be square (same number of rows and columns) and have a non-zero determinant to be invertible.

Formula for Matrix Inverse

The general formula for finding the inverse of a matrix is:

A⁻¹ = (1/det A) × adj(A)

Where:

  • det A is the determinant of matrix A
  • adj(A) is the adjugate (adjoint) of matrix A

2×2 Matrix Inverse

For a 2×2 matrix:

A = | a  b |
    | c  d |

Determinant: det A = ad - bc

Inverse:

A⁻¹ = (1/(ad-bc)) × | d  -b |
                     |-c   a |

3×3 Matrix Inverse

For a 3×3 matrix, the process involves:

  1. Calculate the determinant
  2. Find the matrix of minors
  3. Apply the cofactor sign pattern
  4. Transpose to get the adjugate
  5. Multiply by 1/determinant

Applications

  • Solving linear equations (Ax = b → x = A⁻¹b)
  • Computer graphics (transformation matrices)
  • Cryptography (Hill cipher)
  • Engineering (circuit analysis, control systems)
  • Economics (input-output models)

Limitations

  • Singular matrices (det = 0) have no inverse
  • Numerical instability with near-singular matrices
  • Computational cost increases with matrix size