# 行列計算機

> 最大 6 × 6 の行列の行列式、逆行列、階数、簡約行階段形を、すべての行基本変形付きで計算。転置、積、和にも対応し、厳密な分数を使用します。

操作できるページ：https://www.calcopenly.com/ja/math/matrix-calculator
分野：数学の計算機

The calculator works on matrices up to 6 × 6 in exact fractions. The inverse and the reduced row echelon form come from Gauss–Jordan elimination: [A | I] is row-reduced until the left half is the identity, and the right half is then A⁻¹. The determinant is the product of the pivots from forward elimination, negated once for each row swap; the rank is the number of pivots; and a product has entries (AB)ᵢⱼ = Σₖ aᵢₖbₖⱼ.

Linear algebra courses, 3D graphics transforms and systems of equations are the usual uses. The default matrix [2 1 1; 1 3 2; 1 0 0] has determinant −1, so it is invertible, and its inverse [0 0 1; −2 1 3; 3 −1 −5] has whole-number entries.

Type one row per line, with entries separated by spaces or commas; fractions such as 1/2 stay exact. A matrix with determinant 0 is singular: it has no inverse, and its rank is below its size.

## 入力

- **Calculate** (選択肢：Determinant of A, Inverse of A, Rank of A, Reduced row echelon form of A, Transpose of A, A × B, A + B, A − B)
- **Matrix A**: One row per line (or rows separated by ;), entries separated by spaces or commas; fractions like 1/2 work
- **Matrix B**

## 結果

- 結果 — 主な結果
- det A
- rank A
- trace A

## 計算式

$$
\begin{gathered} A^{-1}:\ [A \mid I] \xrightarrow{\text{row operations}} [I \mid A^{-1}] \\[10pt] (AB)_{ij} = \sum_k a_{ik} b_{kj} \end{gathered}
$$

## 計算例

### Inverse of the default 3 × 3

- Calculate: Inverse of A
- Matrix A: 2 1 1 / 1 3 2 / 1 0 0
- **結果: [0, 0, 1; −2, 1, 3; 3, −1, −5]**
- **det A: -1**
- 照合元：Python fractions Gauss–Jordan on [A | I]; det by cofactor expansion = −1

### Inverse of [4 7; 2 6]

- Calculate: Inverse of A
- Matrix A: 4 7 / 2 6
- **結果: [3/5, −7/10; −1/5, 2/5]**
- **det A: 10**
- 照合元：(1/(ad − bc))·[d −b; −c a] = (1/10)·[6 −7; −2 4]

### Determinant of the default 3 × 3

- Calculate: Determinant of A
- Matrix A: 2 1 1 / 1 3 2 / 1 0 0
- **結果: −1**
- **det A: -1**
- **trace A: 5**
- 照合元：Cofactor expansion: 2·0 − 1·(0 − 2) + 1·(0 − 3) = −1

### Rank of a singular matrix (edge case)

- Calculate: Rank of A
- Matrix A: 1 2 3 / 2 4 6 / 1 1 1
- **結果: 2**
- **rank A: 2**
- **det A: 0**
- 照合元：Row 2 = 2 × row 1; Python fractions RREF has 2 pivots

### [1 2; 3 4] × [5 6; 7 8]

- Calculate: A × B
- Matrix A: 1 2 / 3 4
- Matrix B: 5 6 / 7 8
- **結果: [19, 22; 43, 50]**
- 照合元：Row-by-column products by hand (e.g. 1·5 + 2·7 = 19)

### RREF of the Wikipedia augmented matrix

- Calculate: Reduced row echelon form of A
- Matrix A: 1 2 -1 -4 / 2 3 -1 -11 / -2 0 -3 22
- **結果: [1, 0, 0, −8; 0, 1, 0, 1; 0, 0, 1, −2]**
- **rank A: 3**
- 照合元：Wikipedia “Gaussian elimination” (row reduction example); Python fractions RREF

## よくある質問

### How do you find the inverse of a matrix?

Write A next to the identity matrix, [A | I], and apply row operations until the left half becomes I; the right half is then A⁻¹. A 2 × 2 matrix [a b; c d] has a shortcut: A⁻¹ = (1/(ad − bc)) × [d −b; −c a]. For [4 7; 2 6], ad − bc = 24 − 14 = 10, so A⁻¹ = [3/5 −7/10; −1/5 2/5].

### How do you calculate the determinant of a 3 × 3 matrix?

Expand along a row or column, multiplying each entry by the determinant of its 2 × 2 minor with alternating signs. For [2 1 1; 1 3 2; 1 0 0], the bottom row is quickest because two of its entries are 0: det = 1 × (1 × 2 − 1 × 3) = −1. For larger matrices row reduction reaches the same answer with far fewer operations.

### When does a matrix have no inverse?

When its determinant is 0, which happens exactly when one row or column is a combination of the others. In [1 2 3; 2 4 6; 1 1 1], row 2 is twice row 1, so the rank is 2 rather than 3 and the determinant is 0. Such a matrix is called singular, and a system Ax = b built on it has either no solution or infinitely many.

### How do you multiply two matrices?

Each entry of AB is a row of A times a column of B, summed: (AB)ᵢⱼ = Σₖ aᵢₖbₖⱼ. For [1 2; 3 4] × [5 6; 7 8] the top-left entry is 1 × 5 + 2 × 7 = 19, and the product is [19 22; 43 50]. A needs as many columns as B has rows, and order matters: here BA = [23 34; 31 46].

### What is reduced row echelon form?

A matrix is in reduced row echelon form (RREF) when each non-zero row starts with a 1, that leading 1 is the only non-zero entry in its column, the leading 1s step right going down, and zero rows sit at the bottom. Every matrix has exactly one RREF, and its number of leading 1s is the rank. For an augmented matrix it reads off the solution: [1 0 0 −8; 0 1 0 1; 0 0 1 −2] means x = −8, y = 1, z = −2.

### 「行列計算機」の精度はどのくらいですか？

精度は入力値と計算方法の前提に依存します。十進演算には有効数字50桁を使いますが、推定、数値計算手法、元データの精度はそれより低い場合があります。表示の丸め処理でこれらの制約がなくなるわけではありません。 独立した出典の解答と照合した計算例：8。 例えば、「Inverse of the default 3 × 3」はPython fractions Gauss–Jordan on [A | I]; det by cofactor expansion = −1と照合しています。

### この計算方法の出典は何ですか？

Wikipedia — Gaussian elimination (row reduction and the RREF example); Wolfram MathWorld — Matrix Inverse; G. Strang, Introduction to Linear Algebra (5th ed.), chapters 2–3.

## 出典

- [Wikipedia — Gaussian elimination (row reduction and the RREF example)](https://en.wikipedia.org/wiki/Gaussian_elimination)
- [Wolfram MathWorld — Matrix Inverse](https://mathworld.wolfram.com/MatrixInverse.html)
- G. Strang, Introduction to Linear Algebra (5th ed.), chapters 2–3