1 | initial version |

There is no distinction between row vector and column vector in Sagemath

```
sage: v = vector((1,2,3))
sage: A = matrix(3, [1, -2, 3, 0, 1, -1, 2, 0, -2])
sage: A * v
(6, -1, -4)
sage: v * A
(7, 0, -5)
```

And vectors are different from 1 x n and n x 1 matrices which make a difference between rows and columns

```
sage: col_v = matrix(3, 1, [1, 2, 3])
sage: print(col_v)
[1]
[2]
[3]
sage: A * col_v
[ 6]
[-1]
[-4]
sage: row_v = matrix(1, 3, [1, 2, 3])
sage: print(row_v)
[1 2 3]
sage: row_v * A
[ 7 0 -5]
```

Of course both `A * row_v`

and `col_v * A`

will fail.

2 | No.2 Revision |

There is no distinction between row vector and column vector in Sagemath

```
sage: v = vector((1,2,3))
sage: A = matrix(3, [1, -2, 3, 0, 1, -1, 2, 0, -2])
sage: A * v
(6, -1, -4)
sage: v * A
(7, 0, -5)
```

~~And ~~Sagemath vectors are different ~~from ~~objects than 1 x n ~~and ~~or n x 1 ~~matrices which ~~matrices. Matrices make a difference between rows and columns

```
sage: col_v = matrix(3, 1, [1, 2, 3])
sage: print(col_v)
[1]
[2]
[3]
sage: A * col_v
[ 6]
[-1]
[-4]
sage: row_v = matrix(1, 3, [1, 2, 3])
sage: print(row_v)
[1 2 3]
sage: row_v * A
[ 7 0 -5]
```

Of course both `A * row_v`

and `col_v * A`

will fail.

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