Exactly solve a low dimensional linear system
npm install robust-linear-solverobust-linear-solve
===================
An exact linear solver for low dimensional systems.
``javascript
var linSolve = require("robust-linear-solve")
var A = [ [1, 2, 3],
[3, 2, 1],
[0, 0, 1] ]
var b = [1, 2, 3]
console.log(linSolve(A, b))
`
Output:
`javascript`
[ [ -14 ], [ 23 ], [ -12 ], [ -4 ] ]
`
npm install robust-linear-solve
#### require("robust-linear-solve")(A, b)
Finds the exact solution to a linear system, , using Cramer's rule.
* A is a n-by-n square matrix, encoded as an array of arraysb
* is an n dimensional vector encoded as a length n array
Returns A projective n+1 dimensional vector of non-overlapping increasing sequences representing the exact solution to the system. That is to say, if x is the returned solution then in psuedocode we have the following constraint:
A [ x[0], x[1], ... , x[n-1] ] = b * x[n]
Or in other words, the solution is given by the quotient:
[ x[0] / x[n], x[1] / x[n], .... , x[n-1] / x[n] ]
If the system is not solvable, then the last coefficient, x[n] will be 0.
Note For up to n=5, you can avoid the extra method look up by calling linSolve[n]` directly.