Safe Haskell | None |
---|---|

Language | Haskell98 |

The problem: You have a system of equations, that you have written as a single matrix equation

Ax = b

Where A and b are matrices (b could be a vector, as a special case). You want to find a solution x.

The solution: You can choose between various decompositions, depending on what your matrix A looks like, and depending on whether you favor speed or accuracy. However, let's start with an example that works in all cases, and is a good compromise:

import Data.Eigen.Matrix import Data.Eigen.LA main = do let a = fromList [[1,2,3], [4,5,6], [7,8,10]] b = fromList [[3],[3],[4]] x = solve ColPivHouseholderQR a b putStrLn "Here is the matrix A:" >> print a putStrLn "Here is the vector b:" >> print b putStrLn "The solution is:" >> print x

produces the following output

Here is the matrix A: Matrix 3x3 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 10.0 Here is the vector b: Matrix 3x1 3.0 3.0 4.0 The solution is: Matrix 3x1 -2.0000000000000004 1.0000000000000018 0.9999999999999989

Checking if a solution really exists: Only you know what error margin you want to allow for a solution to be considered valid.

You can compute relative error using `norm (ax - b) / norm b`

formula or use `relativeError`

function which provides the same calculation implemented slightly more efficient.

- data Decomposition
- solve :: Decomposition -> Matrix -> Matrix -> Matrix
- relativeError :: Matrix -> Matrix -> Matrix -> Double
- linearRegression :: [[Double]] -> ([Double], Double)

# Basic linear solving

data Decomposition Source

Decomposition Requirements on the matrix Speed Accuracy PartialPivLU Invertible ++ + FullPivLU None - +++ HouseholderQR None ++ + ColPivHouseholderQR None + ++ FullPivHouseholderQR None - +++ LLT Positive definite +++ + LDLT Positive or negative semidefinite +++ ++ JacobiSVD None - +++ The best way to do least squares solving for square matrices is with a SVD decomposition (JacobiSVD)

PartialPivLU | LU decomposition of a matrix with partial pivoting. |

FullPivLU | LU decomposition of a matrix with complete pivoting. |

HouseholderQR | Householder QR decomposition of a matrix. |

ColPivHouseholderQR | Householder rank-revealing QR decomposition of a matrix with column-pivoting. |

FullPivHouseholderQR | Householder rank-revealing QR decomposition of a matrix with full pivoting. |

LLT | Standard Cholesky decomposition (LL^T) of a matrix. |

LDLT | Robust Cholesky decomposition of a matrix with pivoting. |

JacobiSVD | Two-sided Jacobi SVD decomposition of a rectangular matrix. |

solve :: Decomposition -> Matrix -> Matrix -> Matrix Source

- x = solve d a b
- finds a solution
`x`

of`ax = b`

equation using decomposition`d`

relativeError :: Matrix -> Matrix -> Matrix -> Double Source

- e = relativeError x a b
- computes
`norm (ax - b) / norm b`

where`norm`

is L2 norm

# Multiple linear regression

linearRegression :: [[Double]] -> ([Double], Double) Source

- (coeffs, error) = linearRegression points
- computes multiple linear regression
`y = a1 x1 + a2 x2 + ... + an xn + b`

using`ColPivHouseholderQR`

decomposition

- point format is
`[y, x1..xn]`

- coeffs format is
`[b, a1..an]`

- error is calculated using
`relativeError`

import Data.Eigen.LA main = print $ linearRegression [ [-4.32, 3.02, 6.89], [-3.79, 2.01, 5.39], [-4.01, 2.41, 6.01], [-3.86, 2.09, 5.55], [-4.10, 2.58, 6.32]]

produces the following output

([-2.3466569233817127,-0.2534897541434826,-0.1749653335680988],1.8905965120153139e-3)