# Precision of Matlab Matrix Operators

Let's say we have these 3 matrices:

```
A=[1 3; 2 2]; B=[4 5; 1 3]; C=[0 2; 2 1]
```

I usually try to avoid using inv () or ^ (- 1) and try using the forward and backslash statements instead. But if I want to calculate the following:

```
A*B^(-1)*C
```

which way is best:

`A/B*C`

or

`A*(B\C)`

Even though both use slash operators and no "explicit inverse" is evaluated, the result is different and interestingly, A * (B \ C) computes the same as the one I am trying to avoid, / p>

```
A*inv(B)*C
```

and

```
isequal(A*(B\C),A*inv(B)*C)
```

shows. Can anyone please explain what is going on here and which way should I go? Thank!

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MATLAB has a serious strategy `mldivide`

: see the Algorithm section of the documentation . I can imagine that it decides that 2x2 matrices with inverse transforms is the fastest way to solve it and hence it calls `inv`

and therefore explicitly computes the inverse `B`

. For large matrices this is not the case, and if I choose random `isequal`

4x4 matrices, doing -changes false.

In any case, the "best" approach is highly dependent on your matrices `A`

, `B`

and `C`

.

After some testing, I found it `A*inv(B)*C`

always takes longer than `A*(B\C)`

that, which means it can also be just machine precision (see Dang Khoa's helpful comment). However, there is no single "best" approach for `mldivide`

versus `mrdivide`

.

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