# [racket] matrix-solve and approximation errors

Forgot to mention that with the true value of n=0.82, this of course
returns the correct solution:
(let ([n 0.82 #;(+ (* .9 .9)(* .1 .1))])
(matrix-solve
(matrix [[ 1 0 .9 1]
[ 0 1 .1 1]
[.9 .1 n 1]
[ 1 1 1 0]])
(col-matrix [0 0 0 1])))
; -> (array #[#[0.38] #[0.4866666666666667] #[0.13333333333333333] #[-0.5]])
On Wed, Apr 16, 2014 at 11:10 AM, Laurent <laurent.orseau at gmail.com> wrote:
>* I've just been bitten by a bad case of floating-point error with
*>* `matrix-solve` (and a bad CPU that has some floating-point issues):
*>*
*>* (let ([n 0.8200000000000001 #;(+ (* .9 .9)(* .1 .1))])
*>* (matrix-solve
*>* (matrix [[ 1 0 .9 1]
*>* [ 0 1 .1 1]
*>* [.9 .1 n 1]
*>* [ 1 1 1 0]])
*>* (col-matrix [0 0 0 1])))
*>* ; -> (array #[#[0.0] #[0.5] #[0.0] #[-0.5]])
*>*
*>* But clearly here M×X≠B, as is easily seen on the last row.
*>* I've seen other situations where the approximation leads to an approximate
*>* solution (which is okay of course), but this is the first case I see where
*>* the result is completely off.
*>*
*>* I have no idea if anything can be done about it, though (apart from
*>* throwing my computer through the window and buy a better one).
*>*
*>* Laurent
*>*
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