Matrix Calculator

Matrix is evaluated from Operation, Matrix A - Row 1, Col 1 and Matrix A - Row 1, Col 2. The calculation reports Result [1,1], Result [1,2] and Result [2,1].

Results

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About the Matrix Calculator

Matrix is treated here as a quantitative relation between Operation, Matrix A - Row 1, Col 1, Matrix A - Row 1, Col 2 and Matrix A - Row 2, Col 1 and Result [1,1], Result [1,2], Result [2,1] and Result [2,2].

The calculator uses a multi formula configuration. Each reported value is read as a direct evaluation of the stored rules with the declared field formats and units.

Formula basis:
Add: element-wise sum
Multiply: C[i][j] = row i of A · column j of B
det(A) = ad - bc

Interpret the outputs in the order shown by the result fields. Optional inputs affect only the outputs that depend on those variables.

Formula & How It Works

The calculation applies the following relations exactly as recorded in the metadata:

Add: element-wise sum
Multiply: C[i][j] = row i of A · column j of B
det(A) = ad - bc

Each output field is produced by substituting the supplied inputs into the relevant relation and then applying the declared rounding or text format.

Worked Examples

Example 1: 2D Rotation Matrix — 90°

Inputs

operation: A × B (Multiply) a11: 0 a12: -1 a21: 1 a22: 0 b11: 3 b12: 0 b21: 4 b22: 0
Result [1,1]: -4. Result [1,2]: 0. Result [2,1]: 3. Result [2,2]: 0. det: 1. trace: 0

With Operation = A x B (Multiply), Matrix A - Row 1, Col 1 = 0, Matrix A - Row 1, Col 2 = -1 and Matrix A - Row 2, Col 1 = 1 as the stated inputs, the result is Result [1,1] = -4, Result [1,2] = 0 and Result [2,1] = 3. Each value corresponds to the declared output fields.

Example 2: Network Analysis — Adjacency Matrix Squared

Inputs

operation: A × B (Multiply) a11: 0 a12: 1 a21: 1 a22: 0 b11: 0 b12: 1 b21: 1 b22: 0
Result [1,1]: 1. Result [1,2]: 0. Result [2,1]: 0. Result [2,2]: 1. det: -1. trace: 0

With Operation = A x B (Multiply), Matrix A - Row 1, Col 1 = 0, Matrix A - Row 1, Col 2 = 1 and Matrix A - Row 2, Col 1 = 1 as the stated inputs, the result is Result [1,1] = 1, Result [1,2] = 0 and Result [2,1] = 0. Each value corresponds to the declared output fields.

Example 3: Economics — Input-Output Leontief Model

Inputs

operation: A + B (Add) a11: 0.3 a12: 0.2 a21: 0.1 a22: 0.4 b11: 1 b12: 0 b21: 0 b22: 1
Result [1,1]: 1.3. Result [1,2]: 0.2. Result [2,1]: 0.1. Result [2,2]: 1.4. det: 0.1. trace: 0.7

With Operation = A + B (Add), Matrix A - Row 1, Col 1 = 0.3, Matrix A - Row 1, Col 2 = 0.2 and Matrix A - Row 2, Col 1 = 0.1 as the stated inputs, the result is Result [1,1] = 1.3, Result [1,2] = 0.2 and Result [2,1] = 0.1. Each value corresponds to the declared output fields.

Example 4: Scalar Multiplication — Scale Up Data

Inputs

operation: k × A (Scalar) a11: 3 a12: 1 a21: 4 a22: 2 scalar: 2.5
Result [1,1]: 7.5. Result [1,2]: 2.5. Result [2,1]: 10. Result [2,2]: 5. det: 2. trace: 5

With Operation = k x A (Scalar), Matrix A - Row 1, Col 1 = 3, Matrix A - Row 1, Col 2 = 1 and Matrix A - Row 2, Col 1 = 4 as the stated inputs, the result is Result [1,1] = 7.5, Result [1,2] = 2.5 and Result [2,1] = 10. Each value corresponds to the declared output fields.

Common Use Cases

  • Multiply transformation matrices in graphics
  • Solve simultaneous equations using matrices
  • Apply linear transformations in machine learning
  • Calculate covariance matrices in statistics