Inverse Matrix Calculator

Inverse Matrix Calculator

Calculate the inverse of a square matrix using Gauss-Jordan elimination

Enter the matrix elements row by row. The matrix must be square (same number of rows and columns).

Inverse Matrix Calculator: Complete Linear Algebra Guide

The inverse of a matrix A is a matrix A⁻¹ such that AA⁻¹ = A⁻¹A = I (identity matrix).Matrix inversion is fundamental in linear algebra for solving systems of equations, computer graphics transformations, engineering calculations, and statistical analysis.

Our professional inverse matrix calculator uses Gauss-Jordan elimination to find the inverse of square matrices up to 5×5, providing step-by-step solutions, determinant calculation, and verification of results.

Quick Answer

To find matrix inverse: Use Gauss-Jordan elimination on the augmented matrix [A|I]. For a 2×2 matrix [[a,b],[c,d]], the inverse is (1/det) × [[d,-b],[-c,a]] where det = ad-bc. A matrix is invertible only if its determinant is non-zero.

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Mathematical Foundation

A × A⁻¹ = I

Matrix multiplication property where A is the original matrix, A⁻¹ is its inverse, and I is the identity matrix

Key Concepts:

Determinant

A scalar value that determines if a matrix is invertible. If det(A) = 0, the matrix is singular and has no inverse. The determinant also represents the scaling factor of transformations.

Gauss-Jordan Elimination

Systematic method to find matrix inverse by transforming [A|I] into [I|A⁻¹] using elementary row operations: row swapping, scaling, and row addition/subtraction.

Identity Matrix

Square matrix with 1s on the diagonal and 0s elsewhere. Acts as the multiplicative identity for matrices: AI = IA = A for any compatible matrix A.

Matrix Inversion Methods

Gauss-Jordan Elimination

Most reliable method for computer implementation and education.

1. Form augmented matrix [A|I]
2. Use row operations to get [I|A⁻¹]
3. Right side becomes the inverse matrix
Advantages: Systematic, handles any size, shows all steps

2×2 Matrix Formula

Direct formula for 2×2 matrices only.

For A = [[a,b],[c,d]]
A⁻¹ = (1/(ad-bc)) × [[d,-b],[-c,a]]
Only if det = ad-bc ≠ 0
Limitations: Only works for 2×2 matrices

Adjugate Method

Uses cofactor matrix and adjugate matrix.

A⁻¹ = (1/det(A)) × adj(A)
adj(A) = transpose of cofactor matrix
Cofactor = (-1)^(i+j) × minor
Use case: Theoretical understanding, symbolic computation

Applications of Matrix Inversion

Engineering & Physics

Electrical Circuits

Solve circuit equations, impedance calculations, network analysis using nodal and mesh methods

Structural Analysis

Finite element analysis, stress-strain relationships, structural dynamics and vibration

Control Systems

State-space analysis, feedback control design, system stability and response

Computer Science

Computer Graphics

3D transformations, perspective projections, animation matrices, camera transformations

Machine Learning

Normal equations in linear regression, covariance matrix operations, PCA transformations

Cryptography

Hill cipher encryption/decryption, key generation in matrix-based cryptographic systems

Example Problems with Solutions

Example 1: 2×2 Matrix Inversion

Find the inverse of A = [[2, 1], [1, 1]]

det(A) = (2)(1) - (1)(1) = 2 - 1 = 1
Since det ≠ 0, inverse exists
A⁻¹ = (1/1) × [[1, -1], [-1, 2]]
A⁻¹ = [[1, -1], [-1, 2]]
Verification: AA⁻¹ = [[1, 0], [0, 1]] ✓

Answer: A⁻¹ = [[1, -1], [-1, 2]]

Example 2: Solving Linear System

Solve: 2x + y = 5, x + y = 3 using matrix inversion

System: Ax = b where A = [[2,1],[1,1]], b = [5,3]
Solution: x = A⁻¹b
A⁻¹ = [[1,-1],[-1,2]] (from Example 1)
x = [[1,-1],[-1,2]] × [5,3] = [2,1]
Therefore: x = 2, y = 1

Answer: x = 2, y = 1

Example 3: Non-Invertible Matrix

Check if A = [[1, 2], [2, 4]] is invertible

det(A) = (1)(4) - (2)(2) = 4 - 4 = 0
Since det = 0, matrix is singular
No inverse exists (non-invertible)
Rows are linearly dependent: row 2 = 2 × row 1

Answer: Matrix is not invertible (determinant = 0)

Properties of Matrix Inverses

Basic Properties

(A⁻¹)⁻¹ = A
(AB)⁻¹ = B⁻¹A⁻¹
(Aᵀ)⁻¹ = (A⁻¹)ᵀ
(kA)⁻¹ = (1/k)A⁻¹
det(A⁻¹) = 1/det(A)

Existence Conditions

Required: Matrix must be square
Required: Determinant ≠ 0
Equivalent: Rows linearly independent
Equivalent: Full rank
Equivalent: Non-singular

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