hmatrix-0.16.0.4: Numeric Linear Algebra

Copyright(c) Alberto Ruiz 2006-14
LicenseBSD3
MaintainerAlberto Ruiz
Stabilityprovisional
Safe HaskellNone
LanguageHaskell98

Numeric.LinearAlgebra.HMatrix

Contents

Description

 

Synopsis

Basic types and data processing

Arithmetic and numeric classes

The standard numeric classes are defined elementwise:

>>> vector [1,2,3] * vector [3,0,-2]
fromList [3.0,0.0,-6.0]
>>> matrix 3 [1..9] * ident 3
(3><3)
 [ 1.0, 0.0, 0.0
 , 0.0, 5.0, 0.0
 , 0.0, 0.0, 9.0 ]

In arithmetic operations single-element vectors and matrices (created from numeric literals or using scalar) automatically expand to match the dimensions of the other operand:

>>> 5 + 2*ident 3 :: Matrix Double
(3><3)
 [ 7.0, 5.0, 5.0
 , 5.0, 7.0, 5.0
 , 5.0, 5.0, 7.0 ]
>>> matrix 3 [1..9] + matrix 1 [10,20,30]
(3><3)
 [ 11.0, 12.0, 13.0
 , 24.0, 25.0, 26.0
 , 37.0, 38.0, 39.0 ]

Products

dot

dot :: Numeric t => Vector t -> Vector t -> t Source

<·> :: Numeric t => Vector t -> Vector t -> t infixr 8 Source

infix synonym for dot

>>> vector [1,2,3,4] <·> vector [-2,0,1,1]
5.0
>>> let 𝑖 = 0:+1 :: ℂ
>>> fromList [1+𝑖,1] <·> fromList [1,1+𝑖]
2.0 :+ 0.0

(the dot symbol "·" is obtained by Alt-Gr .)

matrix-vector

app :: Numeric t => Matrix t -> Vector t -> Vector t Source

dense matrix-vector product

(#>) :: Numeric t => Matrix t -> Vector t -> Vector t infixr 8 Source

infix synonym for app

>>> let m = (2><3) [1..]
>>> m
(2><3)
 [ 1.0, 2.0, 3.0
 , 4.0, 5.0, 6.0 ]
>>> let v = vector [10,20,30]
>>> m #> v
fromList [140.0,320.0]

(!#>) :: GMatrix -> Vector Double -> Vector Double infixr 8 Source

general matrix - vector product

>>> let m = mkSparse [((0,999),1.0),((1,1999),2.0)]
>>> m !#> vector [1..2000]
fromList [1000.0,4000.0]

matrix-matrix

mul :: Numeric t => Matrix t -> Matrix t -> Matrix t Source

dense matrix product

(<>) :: Numeric t => Matrix t -> Matrix t -> Matrix t infixr 8 Source

infix synonym of mul

>>> let a = (3><5) [1..]
>>> a
(3><5)
 [  1.0,  2.0,  3.0,  4.0,  5.0
 ,  6.0,  7.0,  8.0,  9.0, 10.0
 , 11.0, 12.0, 13.0, 14.0, 15.0 ]
>>> let b = (5><2) [1,3, 0,2, -1,5, 7,7, 6,0]
>>> b
(5><2)
 [  1.0, 3.0
 ,  0.0, 2.0
 , -1.0, 5.0
 ,  7.0, 7.0
 ,  6.0, 0.0 ]
>>> a <> b
(3><2)
 [  56.0,  50.0
 , 121.0, 135.0
 , 186.0, 220.0 ]

The matrix product is also implemented in the Data.Monoid instance, where single-element matrices (created from numeric literals or using scalar) are used for scaling.

>>> import Data.Monoid as M
>>> let m = matrix 3 [1..6]
>>> m M.<> 2 M.<> diagl[0.5,1,0]
(2><3)
 [ 1.0,  4.0, 0.0
 , 4.0, 10.0, 0.0 ]

mconcat uses optimiseMult to get the optimal association order.

other

outer :: Product t => Vector t -> Vector t -> Matrix t Source

Outer product of two vectors.

>>> fromList [1,2,3] `outer` fromList [5,2,3]
(3><3)
 [  5.0, 2.0, 3.0
 , 10.0, 4.0, 6.0
 , 15.0, 6.0, 9.0 ]

kronecker :: Product t => Matrix t -> Matrix t -> Matrix t Source

Kronecker product of two matrices.

m1=(2><3)
 [ 1.0,  2.0, 0.0
 , 0.0, -1.0, 3.0 ]
m2=(4><3)
 [  1.0,  2.0,  3.0
 ,  4.0,  5.0,  6.0
 ,  7.0,  8.0,  9.0
 , 10.0, 11.0, 12.0 ]
>>> kronecker m1 m2
(8><9)
 [  1.0,  2.0,  3.0,   2.0,   4.0,   6.0,  0.0,  0.0,  0.0
 ,  4.0,  5.0,  6.0,   8.0,  10.0,  12.0,  0.0,  0.0,  0.0
 ,  7.0,  8.0,  9.0,  14.0,  16.0,  18.0,  0.0,  0.0,  0.0
 , 10.0, 11.0, 12.0,  20.0,  22.0,  24.0,  0.0,  0.0,  0.0
 ,  0.0,  0.0,  0.0,  -1.0,  -2.0,  -3.0,  3.0,  6.0,  9.0
 ,  0.0,  0.0,  0.0,  -4.0,  -5.0,  -6.0, 12.0, 15.0, 18.0
 ,  0.0,  0.0,  0.0,  -7.0,  -8.0,  -9.0, 21.0, 24.0, 27.0
 ,  0.0,  0.0,  0.0, -10.0, -11.0, -12.0, 30.0, 33.0, 36.0 ]

cross :: Vector Double -> Vector Double -> Vector Double Source

cross product (for three-element real vectors)

scale :: Container c e => e -> c e -> c e Source

multiplication by scalar

sumElements :: Container c e => c e -> e Source

the sum of elements

prodElements :: Container c e => c e -> e Source

the product of elements

Linear Systems

(<\>) :: (LSDiv c, Field t) => Matrix t -> c t -> c t infixl 7 Source

least squares solution of a linear system, similar to the \ operator of Matlab/Octave (based on linearSolveSVD)

linearSolve :: Field t => Matrix t -> Matrix t -> Maybe (Matrix t) Source

Solve a linear system (for square coefficient matrix and several right-hand sides) using the LU decomposition, returning Nothing for a singular system. For underconstrained or overconstrained systems use linearSolveLS or linearSolveSVD.

linearSolveLS :: Field t => Matrix t -> Matrix t -> Matrix t Source

Least squared error solution of an overconstrained linear system, or the minimum norm solution of an underconstrained system. For rank-deficient systems use linearSolveSVD.

linearSolveSVD :: Field t => Matrix t -> Matrix t -> Matrix t Source

Minimum norm solution of a general linear least squares problem Ax=B using the SVD. Admits rank-deficient systems but it is slower than linearSolveLS. The effective rank of A is determined by treating as zero those singular valures which are less than eps times the largest singular value.

luSolve :: Field t => (Matrix t, [Int]) -> Matrix t -> Matrix t Source

Solution of a linear system (for several right hand sides) from the precomputed LU factorization obtained by luPacked.

cholSolve :: Field t => Matrix t -> Matrix t -> Matrix t Source

Solve a symmetric or Hermitian positive definite linear system using a precomputed Cholesky decomposition obtained by chol.

cgSolve Source

Arguments

:: Bool

is symmetric

-> GMatrix

coefficient matrix

-> Vector Double

right-hand side

-> Vector Double

solution

cgSolve' Source

Arguments

:: Bool

symmetric

-> R

relative tolerance for the residual (e.g. 1E-4)

-> R

relative tolerance for δx (e.g. 1E-3)

-> Int

maximum number of iterations

-> GMatrix

coefficient matrix

-> V

initial solution

-> V

right-hand side

-> [CGState]

solution

Inverse and pseudoinverse

inv :: Field t => Matrix t -> Matrix t Source

Inverse of a square matrix. See also invlndet.

pinv :: Field t => Matrix t -> Matrix t Source

Pseudoinverse of a general matrix with default tolerance (pinvTol 1, similar to GNU-Octave).

pinvTol :: Field t => Double -> Matrix t -> Matrix t Source

pinvTol r computes the pseudoinverse of a matrix with tolerance tol=r*g*eps*(max rows cols), where g is the greatest singular value.

m = (3><3) [ 1, 0,    0
           , 0, 1,    0
           , 0, 0, 1e-10] :: Matrix Double
>>> pinv m
1. 0.           0.
0. 1.           0.
0. 0. 10000000000.
>>> pinvTol 1E8 m
1. 0. 0.
0. 1. 0.
0. 0. 1.

Determinant and rank

rcond :: Field t => Matrix t -> Double Source

Reciprocal of the 2-norm condition number of a matrix, computed from the singular values.

rank :: Field t => Matrix t -> Int Source

Number of linearly independent rows or columns. See also ranksv

det :: Field t => Matrix t -> t Source

Determinant of a square matrix. To avoid possible overflow or underflow use invlndet.

invlndet Source

Arguments

:: Field t 
=> Matrix t 
-> (Matrix t, (t, t))

(inverse, (log abs det, sign or phase of det))

Joint computation of inverse and logarithm of determinant of a square matrix.

Norms

class Normed a where Source

Methods

norm_0 :: a -> Source

norm_1 :: a -> Source

norm_2 :: a -> Source

norm_Inf :: a -> Source

Nullspace and range

orth :: Field t => Matrix t -> Matrix t Source

return an orthonormal basis of the range space of a matrix. See also orthSVD.

nullspace :: Field t => Matrix t -> Matrix t Source

return an orthonormal basis of the null space of a matrix. See also nullspaceSVD.

null1 :: Matrix Double -> Vector Double Source

solution of overconstrained homogeneous linear system

null1sym :: Matrix Double -> Vector Double Source

solution of overconstrained homogeneous symmetric linear system

SVD

svd :: Field t => Matrix t -> (Matrix t, Vector Double, Matrix t) Source

Full singular value decomposition.

a = (5><3)
 [  1.0,  2.0,  3.0
 ,  4.0,  5.0,  6.0
 ,  7.0,  8.0,  9.0
 , 10.0, 11.0, 12.0
 , 13.0, 14.0, 15.0 ] :: Matrix Double
>>> let (u,s,v) = svd a
>>> disp 3 u
5x5
-0.101   0.768   0.614   0.028  -0.149
-0.249   0.488  -0.503   0.172   0.646
-0.396   0.208  -0.405  -0.660  -0.449
-0.543  -0.072  -0.140   0.693  -0.447
-0.690  -0.352   0.433  -0.233   0.398
>>> s
fromList [35.18264833189422,1.4769076999800903,1.089145439970417e-15]
>>> disp 3 v
3x3
-0.519  -0.751   0.408
-0.576  -0.046  -0.816
-0.632   0.659   0.408
>>> let d = diagRect 0 s 5 3
>>> disp 3 d
5x3
35.183  0.000  0.000
 0.000  1.477  0.000
 0.000  0.000  0.000
 0.000  0.000  0.000
>>> disp 3 $ u <> d <> tr v
5x3
 1.000   2.000   3.000
 4.000   5.000   6.000
 7.000   8.000   9.000
10.000  11.000  12.000
13.000  14.000  15.000

thinSVD :: Field t => Matrix t -> (Matrix t, Vector Double, Matrix t) Source

A version of svd which returns only the min (rows m) (cols m) singular vectors of m.

If (u,s,v) = thinSVD m then m == u <> diag s <> tr v.

a = (5><3)
 [  1.0,  2.0,  3.0
 ,  4.0,  5.0,  6.0
 ,  7.0,  8.0,  9.0
 , 10.0, 11.0, 12.0
 , 13.0, 14.0, 15.0 ] :: Matrix Double
>>> let (u,s,v) = thinSVD a
>>> disp 3 u
5x3
-0.101   0.768   0.614
-0.249   0.488  -0.503
-0.396   0.208  -0.405
-0.543  -0.072  -0.140
-0.690  -0.352   0.433
>>> s
fromList [35.18264833189422,1.4769076999800903,1.089145439970417e-15]
>>> disp 3 v
3x3
-0.519  -0.751   0.408
-0.576  -0.046  -0.816
-0.632   0.659   0.408
>>> disp 3 $ u <> diag s <> tr v
5x3
 1.000   2.000   3.000
 4.000   5.000   6.000
 7.000   8.000   9.000
10.000  11.000  12.000
13.000  14.000  15.000

compactSVD :: Field t => Matrix t -> (Matrix t, Vector Double, Matrix t) Source

Similar to thinSVD, returning only the nonzero singular values and the corresponding singular vectors.

a = (5><3)
 [  1.0,  2.0,  3.0
 ,  4.0,  5.0,  6.0
 ,  7.0,  8.0,  9.0
 , 10.0, 11.0, 12.0
 , 13.0, 14.0, 15.0 ] :: Matrix Double
>>> let (u,s,v) = compactSVD a
>>> disp 3 u
5x2
-0.101   0.768
-0.249   0.488
-0.396   0.208
-0.543  -0.072
-0.690  -0.352
>>> s
fromList [35.18264833189422,1.4769076999800903]
>>> disp 3 u
5x2
-0.101   0.768
-0.249   0.488
-0.396   0.208
-0.543  -0.072
-0.690  -0.352
>>> disp 3 $ u <> diag s <> tr v
5x3
 1.000   2.000   3.000
 4.000   5.000   6.000
 7.000   8.000   9.000
10.000  11.000  12.000
13.000  14.000  15.000

singularValues :: Field t => Matrix t -> Vector Double Source

Singular values only.

leftSV :: Field t => Matrix t -> (Matrix t, Vector Double) Source

Singular values and all left singular vectors (as columns).

rightSV :: Field t => Matrix t -> (Vector Double, Matrix t) Source

Singular values and all right singular vectors (as columns).

Eigensystems

eig :: Field t => Matrix t -> (Vector (Complex Double), Matrix (Complex Double)) Source

Eigenvalues (not ordered) and eigenvectors (as columns) of a general square matrix.

If (s,v) = eig m then m <> v == v <> diag s

a = (3><3)
 [ 3, 0, -2
 , 4, 5, -1
 , 3, 1,  0 ] :: Matrix Double
>>> let (l, v) = eig a
>>> putStr . dispcf 3 . asRow $ l
1x3
1.925+1.523i  1.925-1.523i  4.151
>>> putStr . dispcf 3 $ v
3x3
-0.455+0.365i  -0.455-0.365i   0.181
        0.603          0.603  -0.978
 0.033+0.543i   0.033-0.543i  -0.104
>>> putStr . dispcf 3 $ complex a <> v
3x3
-1.432+0.010i  -1.432-0.010i   0.753
 1.160+0.918i   1.160-0.918i  -4.059
-0.763+1.096i  -0.763-1.096i  -0.433
>>> putStr . dispcf 3 $ v <> diag l
3x3
-1.432+0.010i  -1.432-0.010i   0.753
 1.160+0.918i   1.160-0.918i  -4.059
-0.763+1.096i  -0.763-1.096i  -0.433

eigSH :: Field t => Matrix t -> (Vector Double, Matrix t) Source

Eigenvalues and eigenvectors (as columns) of a complex hermitian or real symmetric matrix, in descending order.

If (s,v) = eigSH m then m == v <> diag s <> tr v

a = (3><3)
 [ 1.0, 2.0, 3.0
 , 2.0, 4.0, 5.0
 , 3.0, 5.0, 6.0 ]
>>> let (l, v) = eigSH a
>>> l
fromList [11.344814282762075,0.17091518882717918,-0.5157294715892575]
>>> disp 3 $ v <> diag l <> tr v
3x3
1.000  2.000  3.000
2.000  4.000  5.000
3.000  5.000  6.000

eigSH' :: Field t => Matrix t -> (Vector Double, Matrix t) Source

Similar to eigSH without checking that the input matrix is hermitian or symmetric. It works with the upper triangular part.

eigenvalues :: Field t => Matrix t -> Vector (Complex Double) Source

Eigenvalues (not ordered) of a general square matrix.

eigenvaluesSH :: Field t => Matrix t -> Vector Double Source

Eigenvalues (in descending order) of a complex hermitian or real symmetric matrix.

eigenvaluesSH' :: Field t => Matrix t -> Vector Double Source

Similar to eigenvaluesSH without checking that the input matrix is hermitian or symmetric. It works with the upper triangular part.

geigSH' Source

Arguments

:: Field t 
=> Matrix t

A

-> Matrix t

B

-> (Vector Double, Matrix t) 

Generalized symmetric positive definite eigensystem Av = lBv, for A and B symmetric, B positive definite (conditions not checked).

QR

qr :: Field t => Matrix t -> (Matrix t, Matrix t) Source

QR factorization.

If (q,r) = qr m then m == q <> r, where q is unitary and r is upper triangular.

rq :: Field t => Matrix t -> (Matrix t, Matrix t) Source

RQ factorization.

If (r,q) = rq m then m == r <> q, where q is unitary and r is upper triangular.

qrRaw :: Field t => Matrix t -> (Matrix t, Vector t) Source

qrgr :: Field t => Int -> (Matrix t, Vector t) -> Matrix t Source

generate a matrix with k orthogonal columns from the output of qrRaw

Cholesky

chol :: Field t => Matrix t -> Matrix t Source

Cholesky factorization of a positive definite hermitian or symmetric matrix.

If c = chol m then c is upper triangular and m == ctrans c <> c.

cholSH :: Field t => Matrix t -> Matrix t Source

Similar to chol, without checking that the input matrix is hermitian or symmetric. It works with the upper triangular part.

mbCholSH :: Field t => Matrix t -> Maybe (Matrix t) Source

Similar to cholSH, but instead of an error (e.g., caused by a matrix not positive definite) it returns Nothing.

Hessenberg

hess :: Field t => Matrix t -> (Matrix t, Matrix t) Source

Hessenberg factorization.

If (p,h) = hess m then m == p <> h <> ctrans p, where p is unitary and h is in upper Hessenberg form (it has zero entries below the first subdiagonal).

Schur

schur :: Field t => Matrix t -> (Matrix t, Matrix t) Source

Schur factorization.

If (u,s) = schur m then m == u <> s <> ctrans u, where u is unitary and s is a Shur matrix. A complex Schur matrix is upper triangular. A real Schur matrix is upper triangular in 2x2 blocks.

"Anything that the Jordan decomposition can do, the Schur decomposition can do better!" (Van Loan)

LU

lu :: Field t => Matrix t -> (Matrix t, Matrix t, Matrix t, t) Source

Explicit LU factorization of a general matrix.

If (l,u,p,s) = lu m then m == p <> l <> u, where l is lower triangular, u is upper triangular, p is a permutation matrix and s is the signature of the permutation.

luPacked :: Field t => Matrix t -> (Matrix t, [Int]) Source

Obtains the LU decomposition of a matrix in a compact data structure suitable for luSolve.

Matrix functions

expm :: Field t => Matrix t -> Matrix t Source

Matrix exponential. It uses a direct translation of Algorithm 11.3.1 in Golub & Van Loan, based on a scaled Pade approximation.

sqrtm :: Field t => Matrix t -> Matrix t Source

Matrix square root. Currently it uses a simple iterative algorithm described in Wikipedia. It only works with invertible matrices that have a real solution. For diagonalizable matrices you can try matFunc sqrt.

m = (2><2) [4,9
           ,0,4] :: Matrix Double
>>> sqrtm m
(2><2)
 [ 2.0, 2.25
 , 0.0,  2.0 ]

matFunc :: (Complex Double -> Complex Double) -> Matrix (Complex Double) -> Matrix (Complex Double) Source

Generic matrix functions for diagonalizable matrices. For instance:

logm = matFunc log

Correlation and convolution

corr Source

Arguments

:: (Container Vector t, Product t) 
=> Vector t

kernel

-> Vector t

source

-> Vector t 

correlation

>>> corr (fromList[1,2,3]) (fromList [1..10])
fromList [14.0,20.0,26.0,32.0,38.0,44.0,50.0,56.0]

conv :: (Container Vector t, Product t, Num t) => Vector t -> Vector t -> Vector t Source

convolution (corr with reversed kernel and padded input, equivalent to polynomial product)

>>> conv (fromList[1,1]) (fromList [-1,1])
fromList [-1.0,0.0,1.0]

corrMin :: (Container Vector t, RealElement t, Product t) => Vector t -> Vector t -> Vector t Source

similar to corr, using min instead of (*)

corr2 :: Product a => Matrix a -> Matrix a -> Matrix a Source

2D correlation (without padding)

>>> disp 5 $ corr2 (konst 1 (3,3)) (ident 10 :: Matrix Double)
8x8
3  2  1  0  0  0  0  0
2  3  2  1  0  0  0  0
1  2  3  2  1  0  0  0
0  1  2  3  2  1  0  0
0  0  1  2  3  2  1  0
0  0  0  1  2  3  2  1
0  0  0  0  1  2  3  2
0  0  0  0  0  1  2  3

conv2 Source

Arguments

:: (Num (Matrix a), Product a, Container Vector a) 
=> Matrix a

kernel

-> Matrix a 
-> Matrix a 

2D convolution

>>> disp 5 $ conv2 (konst 1 (3,3)) (ident 10 :: Matrix Double)
12x12
1  1  1  0  0  0  0  0  0  0  0  0
1  2  2  1  0  0  0  0  0  0  0  0
1  2  3  2  1  0  0  0  0  0  0  0
0  1  2  3  2  1  0  0  0  0  0  0
0  0  1  2  3  2  1  0  0  0  0  0
0  0  0  1  2  3  2  1  0  0  0  0
0  0  0  0  1  2  3  2  1  0  0  0
0  0  0  0  0  1  2  3  2  1  0  0
0  0  0  0  0  0  1  2  3  2  1  0
0  0  0  0  0  0  0  1  2  3  2  1
0  0  0  0  0  0  0  0  1  2  2  1
0  0  0  0  0  0  0  0  0  1  1  1

Random arrays

type Seed = Int Source

data RandDist Source

Constructors

Uniform

uniform distribution in [0,1)

Gaussian

normal distribution with mean zero and standard deviation one

Instances

randomVector Source

Arguments

:: Seed 
-> RandDist

distribution

-> Int

vector size

-> Vector Double 

Obtains a vector of pseudorandom elements (use randomIO to get a random seed).

rand :: Int -> Int -> IO (Matrix Double) Source

pseudorandom matrix with uniform elements between 0 and 1

randn :: Int -> Int -> IO (Matrix Double) Source

pseudorandom matrix with normal elements

>>> disp 3 =<< randn 3 5
3x5
0.386  -1.141   0.491  -0.510   1.512
0.069  -0.919   1.022  -0.181   0.745
0.313  -0.670  -0.097  -1.575  -0.583

gaussianSample Source

Arguments

:: Seed 
-> Int

number of rows

-> Vector Double

mean vector

-> Matrix Double

covariance matrix

-> Matrix Double

result

Obtains a matrix whose rows are pseudorandom samples from a multivariate Gaussian distribution.

uniformSample Source

Arguments

:: Seed 
-> Int

number of rows

-> [(Double, Double)]

ranges for each column

-> Matrix Double

result

Obtains a matrix whose rows are pseudorandom samples from a multivariate uniform distribution.

Misc

meanCov :: Matrix Double -> (Vector Double, Matrix Double) Source

Compute mean vector and covariance matrix of the rows of a matrix.

>>> meanCov $ gaussianSample 666 1000 (fromList[4,5]) (diagl[2,3])
(fromList [4.010341078059521,5.0197204699640405],
(2><2)
 [     1.9862461923890056, -1.0127225830525157e-2
 , -1.0127225830525157e-2,     3.0373954915729318 ])

peps :: RealFloat x => x Source

1 + 0.5*peps == 1, 1 + 0.6*peps /= 1

relativeError :: (Normed c t, Num (c t)) => NormType -> c t -> c t -> Double Source

haussholder :: Field a => a -> Vector a -> Matrix a Source

udot :: Product e => Vector e -> Vector e -> e Source

unconjugated dot product

nullspaceSVD Source

Arguments

:: Field t 
=> Either Double Int

Left "numeric" zero (eg. 1*eps), or Right "theoretical" matrix rank.

-> Matrix t

input matrix m

-> (Vector Double, Matrix t)

rightSV of m

-> Matrix t

nullspace

The nullspace of a matrix from its precomputed SVD decomposition.

orthSVD Source

Arguments

:: Field t 
=> Either Double Int

Left "numeric" zero (eg. 1*eps), or Right "theoretical" matrix rank.

-> Matrix t

input matrix m

-> (Matrix t, Vector Double)

leftSV of m

-> Matrix t

orth

The range space a matrix from its precomputed SVD decomposition.

ranksv Source

Arguments

:: Double

numeric zero (e.g. 1*eps)

-> Int

maximum dimension of the matrix

-> [Double]

singular values

-> Int

rank of m

Numeric rank of a matrix from its singular values.

iC :: Source

imaginary unit

Auxiliary classes

class Storable a => Element a Source

Supported matrix elements.

This class provides optimized internal operations for selected element types. It provides unoptimised defaults for any Storable type, so you can create instances simply as: instance Element Foo.

class (Complexable c, Fractional e, Element e) => Container c e Source

Basic element-by-element functions for numeric containers

Minimal complete definition

size', scalar', conj', scale', scaleRecip, addConstant, add, sub, mul, divide, equal, arctan2', cmap', konst', build', atIndex', minIndex', maxIndex', minElement', maxElement', sumElements', prodElements', step', cond', find', assoc', accum'

class (Num e, Element e) => Product e Source

Matrix product and related functions

Minimal complete definition

multiply, absSum, norm1, norm2, normInf

class LSDiv c Source

Minimal complete definition

linSolve

Instances

class Complexable c Source

Structures that may contain complex numbers

Minimal complete definition

toComplex', fromComplex', comp', single', double'

class (Element t, Element (Complex t), RealFloat t) => RealElement t Source

Supported real types

type family RealOf x Source

Instances

type family SingleOf x Source

Instances

type family DoubleOf x Source

Instances

type family IndexOf c Source

Instances

type IndexOf Vector = Int 
type IndexOf Matrix = (Int, Int) 

class (Product t, Convert t, Container Vector t, Container Matrix t, Normed Matrix t, Normed Vector t, Floating t, RealOf t ~ Double) => Field t Source

Generic linear algebra functions for double precision real and complex matrices.

(Single precision data can be converted using single and double).

Minimal complete definition

svd', thinSVD', sv', luPacked', luSolve', mbLinearSolve', linearSolve', cholSolve', linearSolveSVD', linearSolveLS', eig', eigSH'', eigOnly, eigOnlySH, cholSH', mbCholSH', qr', qrgr', hess', schur'

class Transposable m mt | m -> mt, mt -> m where Source

Methods

tr :: m -> mt Source

(conjugate) transpose

Instances

data CGState Source

Constructors

CGState 

Fields

cgp :: V

conjugate gradient

cgr :: V

residual

cgr2 :: R

squared norm of residual

cgx :: V

current solution

cgdx :: R

normalized size of correction

class Testable t where Source

Minimal complete definition

checkT

Methods

checkT :: t -> (Bool, IO ()) Source

ioCheckT :: t -> IO (Bool, IO ()) Source

Instances

Testable GMatrix 
(KnownNat n', KnownNat m') => Testable (L n' m')