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 :: MatrixXd 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

formula or use `norm`

(ax - b) / `norm`

b`relativeError`

function which provides the same calculation implemented slightly more efficient.

- data Decomposition
- solve :: Elem a b => Decomposition -> Matrix a b -> Matrix a b -> Matrix a b
- relativeError :: Elem a b => Matrix a b -> Matrix a b -> Matrix a b -> a
- rank :: Elem a b => Decomposition -> Matrix a b -> Int
- kernel :: Elem a b => Decomposition -> Matrix a b -> Matrix a b
- image :: Elem a b => Decomposition -> Matrix a b -> Matrix a b
- linearRegression :: [[Double]] -> ([Double], Double)

# Basic linear solving

data Decomposition Source #

Decomposition Requirements on the matrix Speed Accuracy Rank Kernel Image 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 :: Elem a b => Decomposition -> Matrix a b -> Matrix a b -> Matrix a b Source #

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

of`ax = b`

equation using decomposition`d`

relativeError :: Elem a b => Matrix a b -> Matrix a b -> Matrix a b -> a Source #

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

where`norm`

is L2 norm

# Rank-revealing decompositions

Certain decompositions are rank-revealing, i.e. are able to compute the `rank`

of a matrix. These are typically also the decompositions that behave best in the face of a non-full-rank matrix (which in the `square`

case means a singular matrix).

import Data.Eigen.Matrix import Data.Eigen.LA main = do let a = fromList [[1,2,5],[2,1,4],[3,0,3]] :: MatrixXd putStrLn "Here is the matrix A:" >> print a putStrLn "The rank of A is:" >> print (rank FullPivLU a) putStrLn "Here is a matrix whose columns form a basis of the null-space of A:" >> print (kernel FullPivLU a) putStrLn "Here is a matrix whose columns form a basis of the column-space of A:" >> print (image FullPivLU a)

produces the following output

Here is the matrix A: Matrix 3x3 1.0 2.0 5.0 2.0 1.0 4.0 3.0 0.0 3.0 The rank of A is: 2 Here is a matrix whose columns form a basis of the null-space of A: Matrix 3x1 0.5000000000000001 1.0 -0.5 Here is a matrix whose columns form a basis of the column-space of A: Matrix 3x2 5.0 1.0 4.0 2.0 3.0 3.0

kernel :: Elem a b => Decomposition -> Matrix a b -> Matrix a b Source #

Return matrix whose columns form a basis of the null-space of `A`

image :: Elem a b => Decomposition -> Matrix a b -> Matrix a b Source #

Return a matrix whose columns form a basis of the column-space of `A`

# Multiple linear regression

A linear regression model that contains more than one predictor variable.

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)