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### Class Index

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# Linear Algebra

Contents:

1. LU Decomposition

2. QR Decomposition

3. QR Decomposition with Column Pivoting

4. Singular Value Decomposition

5. Cholesky Decomposition

6. Tridiagonal Decomposition of Real Symmetric Matrices

7. Tridiagonal Decomposition of Hermitian Matrices

8. Hessenberg Decomposition of Real Matrices

9. Hessenberg-Triangular Decomposition of Real Matrices

10. Bidiagonalization

11. Householder Transformations

12. Householder solver for linear systems

13. Tridiagonal Systems

14. Balancing

15. NArray

## LU Decomposition

- GSL::Linalg::LU.decomp(A)

- GSL::Matrix#LU_decomp

  These method calculate the LU decomposition of the matrix. The returned value is an array of `[LU, perm, sign]`.

  Examples:

1. Singleton method of the `GSL::Linalg::LU` module

       >> m = Matrix[1..9, 3, 3]
       => GSL::Matrix:
       [ 1.000e+00 2.000e+00 3.000e+00
         4.000e+00 5.000e+00 6.000e+00
         7.000e+00 8.000e+00 9.000e+00 ]
       >> lu, perm, sign = Linalg::LU.decomp(m)

2. Instance method of `GSL::Matrix` class

       >> lu, perm, sign = m.LU_decomp

- GSL::Linalg::LU.solve(A, b)

- GSL::Linalg::LU.solve(lu, perm, b)

- GSL::Matrix#LU_solve(b)

- GSL::Linalg::LUMatrix#solve(perm, b)

  The following is an example to solve a linear system

      A x = b, b = [1, 2, 3, 4]

  using LU decomposition.

1. Singleton method of the `GSL::Linalg::LU` module

       A = Matrix[[0.18, 0.60, 0.57, 0.96], [0.41, 0.24, 0.99, 0.58],
                  [0.14, 0.30, 0.97, 0.66], [0.51, 0.13, 0.19, 0.85]]
       lu, perm, sign = A.LU_decomp
       b = Vector[1, 2, 3, 4]
       x = Linalg::LU.solve(lu, perm, b)

2. Instance method of `GSL::Linalg::LUMatrix` class

       lu, perm, sign = A.LU_decomp  # lu is an instance of Linalg::LUMatrix class
       b = Vector[1, 2, 3, 4]
       x = lu.solve(perm, b)

3. Solve directly

       x = Linalg::LU.solve(A, b)  # LU decomposition is calculated internally (A is not modified)

- GSL::Linalg::LU.svx(A, b)

- GSL::Linalg::LU.svx(lu, perm, b)

- GSL::Matrix#svx(b)

- GSL::Linalg::LUMatrix#svx(perm, b)

  These solve the system Ax = b. The input vector `b` is overwitten by the solution `x`.

- GSL::Linalg::LU.refine(A, lu, perm, b, x)

  This method applys an iterative improvement to `x`, the solution of `A x = b`, using the LU decomposition of `A`.

- GSL::Linalg::LU.invert(A)

- GSL::Linalg::LU.invert(lu, perm)

- GSL::Matrix#invert

- GSL::Linalg::LUMatrix#invert(perm)

  These computes and returns the inverse of the matrix.

- GSL::Linalg::LU.det(A)

- GSL::Linalg::LU.det(lu, signum)

- GSL::Matrix#det

- GSL::Linalg::LUMatrix#det(signum)

  These methods return the determinant of the matrix.

### Complex LU decomposition

## QR decomposition

- GSL::Linalg::QR_decomp(A)

- GSL::Linalg::QR.decomp(A)

- GSL::Matrix#QR_decomp

  These compute QR decomposition of the matrix and return an array [QR, tau].

1. Singleton method of the module `GSL::Linalg`

       qr, tau = Linalg::QR_decomp(m)
       p qr.class                 # GSL::Linalg::QRMatrix, subclass of GSL::Matrix
       p tau.class                # GSL::Linalg::TauVector, subclass of GSL::Vector

2. Singleton method of the module `GSL::Linalg:QR`

       qr, tau = Linalg::QR.decomp(m)

3. Instance method of `GSL::Matrix`

       qr, tau = m.QR_decomp

- GSL::Linalg::QR.solve(A, b)

- GSL::Linalg::QR.solve(QR, tau, b)

- GSL::Matrix#QR_solve(b)

- GSL::Linalg::QRMatrix#solve(tau, b)

  Solve the system A x = b using the QR decomposition.

  - Ex1:

        m = Matrix.alloc(...)
        b = Vector.alloc(...)
        x = Linalg::QR.solve(m, b)

  - Ex2:

        x = m.QR_solve(b)

  - Ex3:

        qr, tau = Linalg::QR.decomp(m)   # or m.QR_decomp
        x = Linalg::QR.solve(qr, tau, b)

  - Ex4:

        qr, tau = m.QR_decomp
        x = qr.solve(tau, b)

- GSL::Linalg::QR.svx(A, x)

- GSL::Linalg::QR.svx(QR, tau, x)

- GSL::Matrix#QR_svx(x)

- GSL::Linalg::QRMatrix#svx(tau, x)

  Solve the system A x = b. The input vector `x` is first give by the right-hand side vector `b`, and is overwritten by the solution.

- GSL::Linalg::QR.unpack(QR, tau)

- GSL::Linalg::QRMatrix#unpack(tau)

  Unpack the encoded QR decomposition `QR,tau` and return an array `[Q, R]`.

  Ex:

      >> m = Matrix[1..9, 3, 3]
      => GSL::Matrix:
      [ 1.000e+00 2.000e+00 3.000e+00
        4.000e+00 5.000e+00 6.000e+00
        7.000e+00 8.000e+00 9.000e+00 ]
      >> qr, tau = m.QR_decomp
      >> q, r = qr.unpack(tau)
      >> q*r               # Reconstruct the metrix m
      => GSL::Matrix:
      [ 1.000e+00 2.000e+00 3.000e+00
        4.000e+00 5.000e+00 6.000e+00
        7.000e+00 8.000e+00 9.000e+00 ]

- GSL::Linalg::QR.QRsolve(Q, R, tau)

  This method solves the system `R x = Q^T b` for `x`. It can be used when the QR decomposition of a matrix is available in unpacked form as `Q,R`.

## QR Decomposition with Column Pivoting

- GSL::Linalg::QRPT.decomp(A)

- GSL::Matrix#QRPT_decomp

  These methods factorize the M-by-N matrix `A` into the QRP^T decomposition A = Q R P^T, and return an array `[QR, tau, perm, signum]`.

  - Ex1:

        require("gsl")
        include GSL::Linalg
        m = Matrix.alloc(...)
        qr, tau, perm = QRPT.decomp(m)
        p qr.class                 # GSL::Linalg::QRPTMatrix, subclass of GSL::Matrix

  - Ex2:

        qr, tau, perm = m.QROT_decomp

- GSL::Linalg::QRPT.decomp2(A)

- GSL::Matrix#QRPT_decomp2

  These return an array `[Q, R, tau, perm, signum]`.

  - Ex

        q, r, tau, perm = QRPT.decomp2(m)
        p q.class                  <----- GSL::Linalg::QMatrix
        p r.class                  <----- GSL::Linalg::RMatrix

- GSL::Linalg::QRPT.solve(m, b)

- GSL::Linalg::QRPT.solve(qr, tau, perm, b)

- GSL::Matrix#QRPT_solve(A, b)

- GSL::Linalg::QRPQMatrix#solve(qr, tau, perm, b)

  These methods solve the system `A x = b` using the QRP^T decomposition of `A` into `QR, tau, perm`. The solution `x` is returned as a Vector.

  - Ex1:

        m = Matrix.alloc(...)
        qr, tau, perm = m.QRPT_decomp
        b = Vector.alloc([1, 2, 3, 4])
        x = Linalg::QRPT.solve(qr, tau, perm, b)

  - Ex2:

        x = Linalg::QRPT.solve(m, b)

  - Ex3:

        x = qr.solve(tau, p, b)

  - Ex4:

        x = m.QRPT_solve(b)

- GSL::Linalg::QRPT.svx(m, b)

- GSL::Linalg::QRPT.svx(qr, tau, perm, b)

- GSL::Matrix#QRPT_svx(A, b)

  These methods solve the system `A x = b` using the QRP^T decomposition of `A` into `QR, tau, perm`. The input `b` is overwritten by the solution `x`.

- GSL::Linalg::QRPT.QRsolve(q, r, tau, perm, b)

  This method solves the system `R P^T x = Q^T b` for x. It can be used when the QR decomposition of a matrix is available in unpacked form as `q, r` obtained by the method `decomp2`.

  - Ex:

        q, r, tau, perm = QRPT_decomp2
        x = Linalg::QRPT.QRsolve(q, r, perm, b)

- GSL::Linalg::QRPT.update(q, r, perm, u, v)

- GSL::Linalg::QRPT.Rsolve(qr, perm, b)

- GSL::Linalg::QRPTMatrix#Rsolve(perm, b)

- GSL::Linalg::QRPT.Rsvx(qr, perm, b)

- GSL::Linalg::QRPTMatrix#Rsvx(perm, b)

## Singular Value Decomposition

- GSL::Linalg::SV.decomp(A[, work])

- GSL::Matrix#SV_decomp()

  These methods factorize the M-by-N matrix `A` into the singular value decomposition `A = U S V^T` using the Golub-Reinsch SVD algorithm, and return an array `[U, V, S]`.

  Ex:

      >> m = Matrix[[3, 5, 2], [5, 1, 4], [7, 6, 3]]
      => GSL::Matrix:
      [ 3.000e+00 5.000e+00 2.000e+00
        5.000e+00 1.000e+00 4.000e+00
        7.000e+00 6.000e+00 3.000e+00 ]
      >> u, v, s = m.SV_decomp  # u, v: Matrix, s: Vector (singular values)
      >> u*u.trans              # u is orthnormal
      => GSL::Matrix:
      [  1.000e+00  2.452e-17 -4.083e-16
         2.452e-17  1.000e+00 -3.245e-16
        -4.083e-16 -3.245e-16  1.000e+00 ]
      >> v*v.trans              # v is also orthnormal
      => GSL::Matrix:
      [  1.000e+00  3.555e-17 -1.867e-16
         3.555e-17  1.000e+00 -1.403e-16
        -1.867e-16 -1.403e-16  1.000e+00 ]
      >> u*Matrix.diagonal(s)*v.trans # Reconstruct the matrix
      => GSL::Matrix:
      [ 3.000e+00 5.000e+00 2.000e+00
        5.000e+00 1.000e+00 4.000e+00
        7.000e+00 6.000e+00 3.000e+00 ]

- GSL::Linalg::SV.decomp_mod(A)

- GSL::Matrix#SV_decomp_mod

  These compute the SVD using the modified Golub-Reinsch algorithm, which is faster for M>>N.

- GSL::Linalg::SV.decomp_jacobi(A)

- GSL::Matrix#SV_decomp_jacobi

  These compute the SVD using one-sided Jacobi orthogonalization. The Jacobi method can compute singular values to higher relative accuracy than Golub-Reinsch algorithms.

- GSL::Linalg::SV.solve(A, b)

- GSL::Linalg::SV.solve(U, V, S, b)

- GSL::Matrix#SV_solve(b)

  These methods solve the system `A x = b` using the singular value decomposition `U, S, V` of `A`.

  - Ex1:

        m = Matrix.alloc(...)
        b = Vector.alloc(...)
        u, v, s = GSL::Linalg::SV.decomp(m)
        x = GSL::Linalg::SV.solve(u, v, s, b)

  - Ex2:

        x = GSL::Linalg::SV.solve(m, b)

  - Ex3:

        x = m.SV_solve(b)

## Cholesky Decomposition

A symmetric, positive definite square matrix `A` has a Cholesky decomposition into a product of a lower triangular matrix L and its transpose L^T, as `A = L L^T`. This is sometimes referred to as taking the square-root of a matrix. The Cholesky decomposition can only be carried out when all the eigenvalues of the matrix are positive. This decomposition can be used to convert the linear system `A x = b` into a pair of triangular systems (`L y = b, L^T x = y`), which can be solved by forward and back-substitution.

- GSL::Linalg::Cholesky.decomp(A)

  This method factorizes the positive-definite square matrix `A` into the Cholesky decomposition `A = L L^T`. The upper triangular part of the matrix returned contains L^T, the diagonal terms being identical for both L and L^T. If the matrix is not positive-definite then the decomposition will fail.

  Ex:

      >> m = Matrix.pascal(3)
      => GSL::Matrix
      [  1.000e+00  1.000e+00  1.000e+00
         1.000e+00  2.000e+00  3.000e+00
         1.000e+00  3.000e+00  6.000e+00 ]
      >> c = Linalg::Cholesky.decomp(m)
      => GSL::Linalg::Cholesky::CholeskyMatrix
      [  1.000e+00  1.000e+00  1.000e+00
         1.000e+00  1.000e+00  2.000e+00
         1.000e+00  2.000e+00  1.000e+00 ]
      >> l = c.lower
      => GSL::Matrix
      [  1.000e+00  0.000e+00  0.000e+00
         1.000e+00  1.000e+00  0.000e+00
         1.000e+00  2.000e+00  1.000e+00 ]
      >> (l*l.trans) == m
      => true

- GSL::Linalg::Cholesky.solve(cholesky, b)

- GSL::Linalg::Cholesky.svx(cholesky, x)

  These methods solve the system `A x = b` using the Cholesky decomposition of `A` into the matrix `cholesky` given by `GSL::Linalg::Cholesky.decomp`.

### Complex Cholesky decomposition

## Tridiagonal Decomposition of Real Symmetric Matrices

- GSL::Linalg::Symmtd::decomp(A)

  Factorizes the symmetric square matrix `A` into the symmetric tridiagonal decomposition Q T Q^T, and returns the results `(A', tau)`. On output the diagonal and subdiagonal part of the matrix `A'` contain the tridiagonal matrix `T`. The remaining lower triangular part of the matrix `A'` contains the Householder vectors which, together with the Householder coefficients `tau`, encode the orthogonal matrix `Q`. This storage scheme is the same as used by LAPACK. The upper triangular part of `A` is not referenced.

- GSL::Linalg::Symmtd::unpack(A', tau)

  Unpacks the encoded symmetric tridiagonal decomposition `(A', tau)` obtained from `GSL::Linalg::Symmtd::decomp` into the orthogonal matrix `Q`, the vector of diagonal elements `diag` and the vector of subdiagonal elements `subdiag`.

- GSL::Linalg::Symmtd::unpack_T(A', tau)

  Unpacks the diagonal and subdiagonal of the encoded symmetric tridiagonal decomposition `(A', tau)` obtained from `GSL::Linalg::Symmtd::decomp` into the vectors `diag` and `subdiag`.

## Tridiagonal Decomposition of Hermitian Matrices

- GSL::Linalg::Hermtd::decomp(A)

  Factorizes the hermitian matrix `A` into the symmetric tridiagonal decomposition U T U^T, and returns the result as `(A', tau)`. On output the real parts of the diagonal and subdiagonal part of the matrix `A'` contain the tridiagonal matrix `T`. The remaining lower triangular part of the matrix `A'` contains the Householder vectors which, together with the Householder coefficients `tau`, encode the orthogonal matrix `Q`. This storage scheme is the same as used by LAPACK. The upper triangular part of `A` and imaginary parts of the diagonal are not referenced.

- GSL::Linalg::Hermtd::unpack(A', tau)

  Unpacks the encoded tridiagonal decomposition `(A', tau)` obtained from `GSL::Linalg::Hermtd::decomp` into the unitary matrix `U`, the real vector of diagonal elements `diag` and the real vector of subdiagonal elements `subdiag`.

- GSL::Linalg::Hermtd::unpack_T(A', tau)

  Unpacks the diagonal and subdiagonal of the encoded tridiagonal decomposition `(A, tau)` obtained from the `GSL::Linalg::Hermtd::decomp` into the real vectors `diag` and `subdiag`.

## Hessenberg Decomposition of Real Matrices

- GSL::Linalg::Hessenberg::decomp(A)

- GSL::Linalg::hessenberg_decomp(A)

  Computes the Hessenberg decomposition of the matrix `A` by applying the similarity transformation `H = U^T A U`, and returns the result as `(A', tau`. On output, `H` is stored in the upper portion of `A'`. The information required to construct the matrix `U` is stored in the lower triangular portion of `A'`. `U` is a product of N - 2 Householder matrices. The Householder vectors are stored in the lower portion of `A'` (below the subdiagonal) and the Householder coefficients are stored in the vector `tau`.

- GSL::Linalg::Hessenberg::unpack(A', tau)

- GSL::Linalg::hessenberg_unpack(A', tau)

  Constructs the orthogonal matrix `U` and returns it from the information stored in the Hessenberg matrix `A'` along with the vector `tau`. `A'` and `tau` are outputs from `GSL::Linalg::Hessenberg::decomp`.

- GSL::Linalg::Hessenberg::unpack_accum(A', tau, V = I)

- GSL::Linalg::hessenberg_unpack_accum(A', tau, V = I)

  This method is similar to `GSL::Linalg::Hessenberg::unpack`, except it accumulates the matrix `U` into `V`, so that `V' = VU`, and returns `V`. Setting V to the identity matrix provides the same result `GSL::Linalg::Hessenberg::unpack`.

- GSL::Linalg::Hessenberg::set_zero(A')

- GSL::Linalg::hessenberg_set_zero(A')

  Sets the lower triangular portion of `A'`, below the subdiagonal, to zero. It is useful for clearing out the Householder vectors after calling `GSL::Linalg::Hessenberg::decomp`.

## Hessenberg-Triangular Decomposition of Real Matrices

- GSL::Linalg::hesstri_decomp(A, B)

- GSL::Linalg::hesstri_decomp(A, B, work)

- GSL::Linalg::hesstri_decomp(A, B, U, V)

- GSL::Linalg::hesstri_decomp(A, B, U, V, work)

  Compute the Hessenberg-Triangular decomposition of the matrix pair `(A, B)`, and return `(H, R`. If U and V are provided (they may be null), the similarity transformations are stored in them. `work` is an additional workspace of length `N`.

- GSL::Linalg::hesstri_decomp!(A, B)

- GSL::Linalg::hesstri_decomp!(A, B, work)

- GSL::Linalg::hesstri_decomp!(A, B, U, V)

- GSL::Linalg::hesstri_decomp!(A, B, U, V, work)

  Compute the Hessenberg-Triangular decomposition of the matrix pair `(A, B)`. On output, `H` is stored in `A`, and `R` is stored in `B`. If U and V are provided (they may be null), the similarity transformations are stored in them. `work` is an additional workspace of length `N`.

## Bidiagonalization

- GSL::Linalg::Bidiag::decomp!(A)

- GSL::Linalg::bidiag_decomp!(A)

- GSL::Linalg::Bidiag::decomp(A)

- GSL::Linalg::bidiag_decomp(A)

- GSL::Linalg::Bidiag::unpack

- GSL::Linalg::bidiag_unpack

- GSL::Linalg::Bidiag::unpack2

- GSL::Linalg::bidiag_unpack2

- GSL::Linalg::Bidiag::unpack_B

- GSL::Linalg::bidiag_unpack_B

## Householder Transformations

- GSL::Linalg::Householder::transform(v)

- GSL::Linalg::HH::transform(v)

- GSL::Vector#householder_transform

  These methods prepare a Householder transformation P = I - tau v v^T which can be used to zero all the elements of the input vector except the first. On output the transformation is stored in the vector `v` and the scalar tau is returned.

- GSL::Linalg::Householder::hm(tau, v, A)

- GSL::Linalg::HH::hm(tau, v, A)

  These methods apply the Householder matrix P defined by the scalar `tau` and the vector `v` to the left-hand side of the matrix `A`. On output the result P A is stored in `A`.

- GSL::Linalg::Householder::mh(tau, v, A)

- GSL::Linalg::HH::mh(tau, v, A)

  These methods apply the Householder matrix P defined by the scalar `tau` and the vector `v` to the right-hand side of the matrix `A`. On output the result A P is stored in `A`.

- GSL::Linalg::Householder::hv(tau, v, w)

- GSL::Linalg::HH::hv(tau, v, w)

  These methods apply the Householder transformation P defined by the scalar `tau` and the vector `v` to the vector `w`. On output the result P w is stored in `w`.

## Householder solver for linear systems

- GSL::Linalg::HH.solve(A, b)

- GSL::Matrix#HH_solve(b)

  These methods solve the system `A x = b` directly using Householder transformations. The matrix `A` is not modified.

- GSL::Linalg::HH.solve!(A, b)

- GSL::Matrix#HH_solve!(b)

  These methods solve the system `A x = b` directly using Householder transformations. The matrix `A` is destroyed by the Householder transformations.

- GSL::Linalg::HH.svx(A, b)

- GSL::Matrix#HH_svx(b)

  These methods solve the system `A x = b` in-place directly using Householder transformations. The input vector `b` is replaced by the solution.

## Tridiagonal Systems

- GSL::Linglg::solve_tridiag(diag, e, f, b)

- GSL::Linglg::Tridiag::solve(diag, e, f, b)

  These methods solve the general N-by-N system A x = b where `A` is tridiagonal ( N >= 2). The super-diagonal and sub-diagonal vectors `e` and `f` must be one element shorter than the diagonal vector `diag`. The form of `A` for the 4-by-4 case is shown below,

      A = ( d_0 e_0  0   0  )
          ( f_0 d_1 e_1  0  )
          (  0  f_1 d_2 e_2 )
          (  0   0  f_2 d_3 )

- GSL::Linglg::solve_symm_tridiag(diag, e, b)

- GSL::Linglg::Tridiag::solve_symm(diag, e, b)

  These methods solve the general N-by-N system A x = b where `A` is symmetric tridiagonal ( N >= 2). The off-diagonal vector `e` must be one element shorter than the diagonal vector `diag`. The form of `A` for the 4-by-4 case is shown below,

      A = ( d_0 e_0  0   0  )
          ( e_0 d_1 e_1  0  )
          (  0  e_1 d_2 e_2 )
          (  0   0  e_2 d_3 )

- GSL::Linglg::solve_cyc_tridiag(diag, e, f, b)

- GSL::Linglg::Tridiag::solve_cyc(diag, e, f, b)

  These methods solve the general N-by-N system A x = b where `A` is cyclic tridiagonal ( N >= 3). The cyclic super-diagonal and sub-diagonal vectors `e` and `f` must have the same number of elements as the diagonal vector `diag`. The form of `A` for the 4-by-4 case is shown below,

      A = ( d_0 e_0  0  f_3 )
          ( f_0 d_1 e_1  0  )
          (  0  f_1 d_2 e_2 )
          ( e_3  0  f_2 d_3 )

- GSL::Linglg::solve_symm_cyc_tridiag(diag, e, b)

- GSL::Linglg::Tridiag::solve_symm_cyc(diag, e, b)

  These methods solve the general N-by-N system A x = b where `A` is symmetric cyclic tridiagonal ( N >= 3). The cyclic off-diagonal vector `e` must have the same number of elements as the diagonal vector `diag`. The form of `A` for the 4-by-4 case is shown below,

      A = ( d_0 e_0  0  e_3 )
          ( e_0 d_1 e_1  0  )
          (  0  e_1 d_2 e_2 )
          ( e_3  0  e_2 d_3 )

## Balancing

The process of balancing a matrix applies similarity transformations to make the rows and columns have comparable norms. This is useful, for example, to reduce roundoff errors in the solution of eigenvalue problems. Balancing a matrix `A` consists of replacing `A` with a similar matrix where `D` is a diagonal matrix whose entries are powers of the floating point radix.

- GSL::Linalg::balance(A)

- GSL::Linalg::balance(A, D)

  Calculates the balanced counterpart of `A` and the diagonal elements of the similarity transformation. The result is returned as `(A', D)`.

- GSL::Linalg::balance!(A)

- GSL::Linalg::balance!(A, D)

  Replaces the matrix `A` with its balanced counterpart and stores the diagonal elements of the similarity transformation into the vector `D`.

## NArray

The following Linalg methods can handle NArray objects:

- GSL::Linalg |

  - LU |

    - LU.decomp(m)

    - LU.solve(lu, b)

    - LU.svx(lu, bx)

    - LU.det(lu, sign)

    - LU.lndet(lu)

    - LU.invert(lu, perm)

  - QR |

    - QR.decomp(m)

    - QR.solve(qr, tau, b)

    - QR.svx(qr, tau, bx)

  - SV |

    - SV.decomp(m)

    - SV.solve(u, v, s, b)

    - SV.svx(u, v, s, bx)

  - Cholesky |

    - Cholesky.decomp(m)

    - Cholesky.solve(u, v, s, b)

    - Cholesky.svx(u, v, s, bx)

  - HH |

    - HH.solve(m, b)

    - HH.svx(m, bx)

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