How can I solve , where and and in the **least squares sense** in matlab?

So I'd like to have the minimizing as output.

Answer:

Rewrite the quantity to minimise as

`||Xa - b||^2`

`=`

(definition of the Frobenius norm)

`Tr{(Xa - b) (Xa - b)'}`

`=`

(expand matrix-product expression)

`Tr{Xaa'X' - ba'X' - Xab' + bb'}`

`=`

(linearity of the trace operator)

`Tr{Xaa'X'} - Tr{ba'X'} - Tr{Xab'} + Tr{bb'}`

`=`

(trace of transpose of a matrix = trace of the matrix)

`Tr{Xaa'X'} - 2 Tr{ba'X'} + Tr{bb'}`

where `'`

denotes the transpose operator (because all matrices involved are real, transpose and conjugate transpose are the same). Now, if you refer to section 2.5 of the Matrix Cookbook, you'll find that

- the derivative of
`Tr{Xaa'X'}`

is`2Xaa'`

(see equation 111), - the derivative of
`Tr{ba'X'}`

is`ba'`

(see equation 104), - the derivative of
`Tr{bb'}`

is`0`

(because this expression doesn't depend on`X`

).

(All derivatives are with respect to matrix `X`

).

Therefore, the matrix that minimises the quantity of interest satisfies

```
2Xaa' = 2ba'
Xaa' = ba'
```

Therefore, you can use MATLAB's matrix right-division operator, `/`

, to compute `X`

:

```
X = b * a' / (a * a');
```

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