# The LAPACK Interface

The module `cvxopt.lapack` includes functions for solving dense sets of linear equations, for the corresponding matrix factorizations (LU, Cholesky, LDLT), for solving least-squares and least-norm problems, for QR factorization, for symmetric eigenvalue problems, singular value decomposition, and Schur factorization.

In this chapter we briefly describe the Python calling sequences. For further details on the underlying LAPACK functions we refer to the LAPACK Users’ Guide and manual pages.

The BLAS conventional storage scheme of the section Matrix Classes is used. As in the previous chapter, we omit from the function definitions less important arguments that are useful for selecting submatrices. The complete definitions are documented in the docstrings in the source code.

See also

LAPACK Users’ Guide, Third Edition, SIAM, 1999

## General Linear Equations

cvxopt.lapack.gesv(A, B[, ipiv = None])

  Solves

  where and are real or complex matrices, with square and nonsingular.

  The arguments `A` and `B` must have the same type (`'d'` or `'z'`). On entry, `B` contains the right-hand side ; on exit it contains the solution . The optional argument `ipiv` is an integer matrix of length at least . If `ipiv` is provided, then `gesv` solves the system, replaces `A` with the triangular factors in an LU factorization, and returns the permutation matrix in `ipiv`. If `ipiv` is not specified, then `gesv` solves the system but does not return the LU factorization and does not modify `A`.

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.getrf(A, ipiv)

  LU factorization of a general, possibly rectangular, real or complex matrix,

  where is by .

  The argument `ipiv` is an integer matrix of length at least min{, }. On exit, the lower triangular part of `A` is replaced by , the upper triangular part by , and the permutation matrix is returned in `ipiv`.

  Raises an `ArithmeticError` if the matrix is not full rank.

cvxopt.lapack.getrs(A, ipiv, B[, trans = 'N'])

  Solves a general set of linear equations

  given the LU factorization computed by `gesv` or `getrf`.

  On entry, `A` and `ipiv` must contain the factorization as computed by `gesv` or `getrf`. On entry, `B` contains the right-hand side ; on exit it contains the solution . `B` must have the same type as `A`.

cvxopt.lapack.getri(A, ipiv)

  Computes the inverse of a matrix.

  On entry, `A` and `ipiv` must contain the factorization as computed by `gesv` or `getrf`. On exit, `A` contains the matrix inverse.

In the following example we compute

for randomly generated problem data, factoring the coefficient matrix once.

    >>> from cvxopt import matrix, normal
    >>> from cvxopt.lapack import gesv, getrs
    >>> n = 10
    >>> A = normal(n,n)
    >>> b = normal(n)
    >>> ipiv = matrix(0, (n,1))
    >>> x = +b
    >>> gesv(A, x, ipiv)               # x = A^{-1}*b
    >>> x2 = +b
    >>> getrs(A, ipiv, x2, trans='T')  # x2 = A^{-T}*b
    >>> x += x2

Separate functions are provided for equations with band matrices.

cvxopt.lapack.gbsv(A, kl, B[, ipiv = None])

  Solves

  where and are real or complex matrices, with by and banded with subdiagonals.

  The arguments `A` and `B` must have the same type (`'d'` or `'z'`). On entry, `B` contains the right-hand side ; on exit it contains the solution . The optional argument `ipiv` is an integer matrix of length at least . If `ipiv` is provided, then `A` must have rows. On entry the diagonals of are stored in rows to of `A`, using the BLAS format for general band matrices (see the section Matrix Classes). On exit, the factorization is returned in `A` and `ipiv`. If `ipiv` is not provided, then `A` must have rows. On entry the diagonals of are stored in the rows of `A`, following the standard BLAS format for general band matrices. In this case, `gbsv` does not modify `A` and does not return the factorization.

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.gbtrf(A, m, kl, ipiv)

  LU factorization of a general by real or complex band matrix with subdiagonals.

  The matrix is stored using the BLAS format for general band matrices (see the section Matrix Classes), by providing the diagonals (stored as rows of a by matrix `A`), the number of rows , and the number of subdiagonals . The argument `ipiv` is an integer matrix of length at least min{, }. On exit, `A` and `ipiv` contain the details of the factorization.

  Raises an `ArithmeticError` if the matrix is not full rank.

cvxopt.lapack.gbtrs({A, kl, ipiv, B[, trans = 'N'])

  Solves a set of linear equations

  with a general band matrix with subdiagonals, given the LU factorization computed by `gbsv` or `gbtrf`.

  On entry, `A` and `ipiv` must contain the factorization as computed by `gbsv` or `gbtrf`. On entry, `B` contains the right-hand side ; on exit it contains the solution . `B` must have the same type as `A`.

As an example, we solve a linear equation with

    >>> from cvxopt import matrix
    >>> from cvxopt.lapack import gbsv, gbtrf, gbtrs
    >>> n, kl, ku = 4, 2, 1
    >>> A = matrix([[0., 1., 3., 6.], [2., 4., 7., 10.], [5., 8., 11., 0.], [9., 12., 0., 0.]])
    >>> x = matrix(1.0, (n,1))
    >>> gbsv(A, kl, x)
    >>> print(x)
    [ 7.14e-02]
    [ 4.64e-01]
    [-2.14e-01]
    [-1.07e-01]

The code below illustrates how one can reuse the factorization returned by `gbsv`.

    >>> Ac = matrix(0.0, (2*kl+ku+1,n))
    >>> Ac[kl:,:] = A
    >>> ipiv = matrix(0, (n,1))
    >>> x = matrix(1.0, (n,1))
    >>> gbsv(Ac, kl, x, ipiv)                 # solves A*x = 1
    >>> print(x)
    [ 7.14e-02]
    [ 4.64e-01]
    [-2.14e-01]
    [-1.07e-01]
    >>> x = matrix(1.0, (n,1))
    >>> gbtrs(Ac, kl, ipiv, x, trans='T')     # solve A^T*x = 1
    >>> print(x)
    [ 7.14e-02]
    [ 2.38e-02]
    [ 1.43e-01]
    [-2.38e-02]

An alternative method uses `gbtrf` for the factorization.

    >>> Ac[kl:,:] = A
    >>> gbtrf(Ac, n, kl, ipiv)
    >>> x = matrix(1.0, (n,1))
    >>> gbtrs(Ac, kl, ipiv, x)                # solve A^T*x = 1
    >>> print(x)
    [ 7.14e-02]
    [ 4.64e-01]
    [-2.14e-01]
    [-1.07e-01]
    >>> x = matrix(1.0, (n,1))
    >>> gbtrs(Ac, kl, ipiv, x, trans='T')     # solve A^T*x = 1
    >>> print(x)
    [ 7.14e-02]
    [ 2.38e-02]
    [ 1.43e-01]
    [-2.38e-02]

The following functions can be used for tridiagonal matrices. They use a simpler matrix format, with the diagonals stored in three separate vectors.

cvxopt.lapack.gtsv(dl, d, du, B))

  Solves

  where is an by tridiagonal matrix.

  The subdiagonal of is stored as a matrix `dl` of length , the diagonal is stored as a matrix `d` of length , and the superdiagonal is stored as a matrix `du` of length . The four arguments must have the same type (`'d'` or `'z'`). On exit `dl`, `d`, `du` are overwritten with the details of the LU factorization of . On entry, `B` contains the right-hand side ; on exit it contains the solution .

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.gttrf(dl, d, du, du2, ipiv)

  LU factorization of an by tridiagonal matrix.

  The subdiagonal of is stored as a matrix `dl` of length , the diagonal is stored as a matrix `d` of length , and the superdiagonal is stored as a matrix `du` of length . `dl`, `d` and `du` must have the same type. `du2` is a matrix of length , and of the same type as `dl`. `ipiv` is an `'i'` matrix of length . On exit, the five arguments contain the details of the factorization.

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.gttrs(dl, d, du, du2, ipiv, B[, trans = 'N'])

  Solves a set of linear equations

  where is an by tridiagonal matrix.

  The arguments `dl`, `d`, `du`, `du2`, and `ipiv` contain the details of the LU factorization as returned by `gttrf`. On entry, `B` contains the right-hand side ; on exit it contains the solution . `B` must have the same type as the other arguments.

## Positive Definite Linear Equations

cvxopt.lapack.posv(A, B[, uplo = 'L'])

  Solves

  where is a real symmetric or complex Hermitian positive definite matrix.

  On exit, `B` is replaced by the solution, and `A` is overwritten with the Cholesky factor. The matrices `A` and `B` must have the same type (`'d'` or `'z'`).

  Raises an `ArithmeticError` if the matrix is not positive definite.

cvxopt.lapack.potrf(A[, uplo = 'L'])

  Cholesky factorization

  of a positive definite real symmetric or complex Hermitian matrix .

  On exit, the lower triangular part of `A` (if `uplo` is `'L'`) or the upper triangular part (if `uplo` is `'U'`) is overwritten with the Cholesky factor or its (conjugate) transpose.

  Raises an `ArithmeticError` if the matrix is not positive definite.

cvxopt.lapack.potrs(A, B[, uplo = 'L'])

  Solves a set of linear equations

  with a positive definite real symmetric or complex Hermitian matrix, given the Cholesky factorization computed by `posv` or `potrf`.

  On entry, `A` contains the triangular factor, as computed by `posv` or `potrf`. On exit, `B` is replaced by the solution. `B` must have the same type as `A`.

cvxopt.lapack.potri(A[, uplo = 'L'])

  Computes the inverse of a positive definite matrix.

  On entry, `A` contains the Cholesky factorization computed by `potrf` or `posv`. On exit, it contains the matrix inverse.

As an example, we use `posv` to solve the linear system

(1)

by block-elimination. We first pick a random problem.

    >>> from cvxopt import matrix, div, normal, uniform
    >>> from cvxopt.blas import syrk, gemv
    >>> from cvxopt.lapack import posv
    >>> m, n = 100, 50
    >>> A = normal(m,n)
    >>> b1, b2 = normal(m), normal(n)
    >>> d = uniform(m)

We then solve the equations

    >>> Asc = div(A, d[:, n*[0]])                # Asc := diag(d)^{-1}*A
    >>> B = matrix(0.0, (n,n))
    >>> syrk(Asc, B, trans='T')                  # B := Asc^T * Asc = A^T * diag(d)^{-2} * A
    >>> x1 = div(b1, d)                          # x1 := diag(d)^{-1}*b1
    >>> x2 = +b2
    >>> gemv(Asc, x1, x2, trans='T', beta=1.0)   # x2 := x2 + Asc^T*x1 = b2 + A^T*diag(d)^{-2}*b1
    >>> posv(B, x2)                              # x2 := B^{-1}*x2 = B^{-1}*(b2 + A^T*diag(d)^{-2}*b1)
    >>> gemv(Asc, x2, x1, beta=-1.0)             # x1 := Asc*x2 - x1 = diag(d)^{-1} * (A*x2 - b1)
    >>> x1 = div(x1, d)                          # x1 := diag(d)^{-1}*x1 = diag(d)^{-2} * (A*x2 - b1)

There are separate routines for equations with positive definite band matrices.

cvxopt.lapack.pbsv(A, B[, uplo='L'])

  Solves

  where is a real symmetric or complex Hermitian positive definite band matrix.

  On entry, the diagonals of are stored in `A`, using the BLAS format for symmetric or Hermitian band matrices (see section Matrix Classes). On exit, `B` is replaced by the solution, and `A` is overwritten with the Cholesky factor (in the BLAS format for triangular band matrices). The matrices `A` and `B` must have the same type (`'d'` or `'z'`).

  Raises an `ArithmeticError` if the matrix is not positive definite.

cvxopt.lapack.pbtrf(A[, uplo = 'L'])

  Cholesky factorization

  of a positive definite real symmetric or complex Hermitian band matrix .

  On entry, the diagonals of are stored in `A`, using the BLAS format for symmetric or Hermitian band matrices. On exit, `A` contains the Cholesky factor, in the BLAS format for triangular band matrices.

  Raises an `ArithmeticError` if the matrix is not positive definite.

cvxopt.lapack.pbtrs(A, B[, uplo = 'L'])

  Solves a set of linear equations

  with a positive definite real symmetric or complex Hermitian band matrix, given the Cholesky factorization computed by `pbsv` or `pbtrf`.

  On entry, `A` contains the triangular factor, as computed by `pbsv` or `pbtrf`. On exit, `B` is replaced by the solution. `B` must have the same type as `A`.

The following functions are useful for tridiagonal systems.

cvxopt.lapack.ptsv(d, e, B)

  Solves

  where is an by positive definite real symmetric or complex Hermitian tridiagonal matrix.

  The diagonal of is stored as a `'d'` matrix `d` of length and its subdiagonal as a `'d'` or `'z'` matrix `e` of length . The arguments `e` and `B` must have the same type. On exit `d` contains the diagonal elements of in the LDLT or LDLH factorization of , and `e` contains the subdiagonal elements of the unit lower bidiagonal matrix . `B` is overwritten with the solution . Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.pttrf(d, e)

  LDLT or LDLH factorization of an by positive definite real symmetric or complex Hermitian tridiagonal matrix .

  On entry, the argument `d` is a `'d'` matrix with the diagonal elements of . The argument `e` is `'d'` or `'z'` matrix containing the subdiagonal of . On exit `d` contains the diagonal elements of , and `e` contains the subdiagonal elements of the unit lower bidiagonal matrix .

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.pttrs(d, e, B[, uplo = 'L'])

  Solves a set of linear equations

  where is an by positive definite real symmetric or complex Hermitian tridiagonal matrix, given its LDLT or LDLH factorization.

  The argument `d` is the diagonal of the diagonal matrix . The argument `uplo` only matters for complex matrices. If `uplo` is `'L'`, then on exit `e` contains the subdiagonal elements of the unit bidiagonal matrix . If `uplo` is `'U'`, then `e` contains the complex conjugates of the elements of the unit bidiagonal matrix . On exit, `B` is overwritten with the solution . `B` must have the same type as `e`.

## Symmetric and Hermitian Linear Equations

cvxopt.lapack.sysv(A, B[, ipiv = None, uplo = 'L'])

  Solves

  where is a real or complex symmetric matrix of order .

  On exit, `B` is replaced by the solution. The matrices `A` and `B` must have the same type (`'d'` or `'z'`). The optional argument `ipiv` is an integer matrix of length at least equal to . If `ipiv` is provided, `sysv` solves the system and returns the factorization in `A` and `ipiv`. If `ipiv` is not specified, `sysv` solves the system but does not return the factorization and does not modify `A`.

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.sytrf(A, ipiv[, uplo = 'L'])

  LDLT factorization

  of a real or complex symmetric matrix of order .

  `ipiv` is an `'i'` matrix of length at least . On exit, `A` and `ipiv` contain the factorization.

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.sytrs(A, ipiv, B[, uplo = 'L'])

  Solves

  given the LDLT factorization computed by `sytrf` or `sysv`. `B` must have the same type as `A`.

cvxopt.lapack.sytri(A, ipiv[, uplo = 'L'])

  Computes the inverse of a real or complex symmetric matrix.

  On entry, `A` and `ipiv` contain the LDLT factorization computed by `sytrf` or `sysv`. On exit, `A` contains the inverse.

cvxopt.lapack.hesv(A, B[, ipiv = None, uplo = 'L'])

  Solves

  where is a real symmetric or complex Hermitian of order .

  On exit, `B` is replaced by the solution. The matrices `A` and `B` must have the same type (`'d'` or `'z'`). The optional argument `ipiv` is an integer matrix of length at least . If `ipiv` is provided, then `hesv` solves the system and returns the factorization in `A` and `ipiv`. If `ipiv` is not specified, then `hesv` solves the system but does not return the factorization and does not modify `A`.

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.hetrf(A, ipiv[, uplo = 'L'])

  LDLH factorization

  of a real symmetric or complex Hermitian matrix of order . `ipiv` is an `'i'` matrix of length at least . On exit, `A` and `ipiv` contain the factorization.

  Raises an `ArithmeticError` if the matrix is singular.

cvxopt.lapack.hetrs(A, ipiv, B[, uplo = 'L'])

  Solves

  given the LDLH factorization computed by `hetrf` or `hesv`.

cvxopt.lapack.hetri(A, ipiv[, uplo = 'L'])

  Computes the inverse of a real symmetric or complex Hermitian matrix.

  On entry, `A` and `ipiv` contain the LDLH factorization computed by `hetrf` or `hesv`. On exit, `A` contains the inverse.

As an example we solve the KKT system (1).

    >>> from cvxopt.lapack import sysv
    >>> K = matrix(0.0, (m+n,m+n))
    >>> K[: (m+n)*m : m+n+1] = -d**2
    >>> K[:m, m:] = A
    >>> x = matrix(0.0, (m+n,1))
    >>> x[:m], x[m:] = b1, b2
    >>> sysv(K, x, uplo='U')

## Triangular Linear Equations

cvxopt.lapack.trtrs(A, B[, uplo = 'L', trans = 'N', diag = 'N'])

  Solves a triangular set of equations

  where is real or complex and triangular of order , and is a matrix with rows.

  `A` and `B` are matrices with the same type (`'d'` or `'z'`). `trtrs` is similar to `blas.trsm`, except that it raises an `ArithmeticError` if a diagonal element of `A` is zero (whereas `blas.trsm` returns `inf` values).

cvxopt.lapack.trtri(A[, uplo = 'L', diag = 'N'])

  Computes the inverse of a real or complex triangular matrix . On exit, `A` contains the inverse.

cvxopt.lapack.tbtrs(A, B[, uplo = 'L', trans = 'T', diag = 'N'])

  Solves a triangular set of equations

  where is real or complex triangular band matrix of order , and is a matrix with rows.

  The diagonals of are stored in `A` using the BLAS conventions for triangular band matrices. `A` and `B` are matrices with the same type (`'d'` or `'z'`). On exit, `B` is replaced by the solution .

## Least-Squares and Least-Norm Problems

cvxopt.lapack.gels(A, B[, trans = 'N'])

  Solves least-squares and least-norm problems with a full rank by matrix .

  1. `trans` is `'N'`. If is greater than or equal to , `gels` solves the least-squares problem

     If is less than or equal to , `gels` solves the least-norm problem

  2. `trans` is `'T'` or `'C'` and `A` and `B` are real. If is greater than or equal to , `gels` solves the least-norm problem

     If is less than or equal to , `gels` solves the least-squares problem

  3. `trans` is `'C'` and `A` and `B` are complex. If is greater than or equal to , `gels` solves the least-norm problem

     If is less than or equal to , `gels` solves the least-squares problem

  `A` and `B` must have the same typecode (`'d'` or `'z'`). `trans` = `'T'` is not allowed if `A` is complex. On exit, the solution is stored as the leading submatrix of `B`. The matrix `A` is overwritten with details of the QR or the LQ factorization of .

  Note that `gels` does not check whether is full rank.

The following functions compute QR and LQ factorizations.

cvxopt.lapack.geqrf(A, tau)

  QR factorization of a real or complex matrix `A`:

  If is by , then is by and orthogonal/unitary, and is by and upper triangular (if is greater than or equal to ), or upper trapezoidal (if is less than or equal to ).

  `tau` is a matrix of the same type as `A` and of length min{, }. On exit, is stored in the upper triangular/trapezoidal part of `A`. The matrix is stored as a product of min{, } elementary reflectors in the first min{, } columns of `A` and in `tau`.

cvxopt.lapack.gelqf(A, tau)

  LQ factorization of a real or complex matrix `A`:

  If is by , then is by and orthogonal/unitary, and is by and lower triangular (if is less than or equal to ), or lower trapezoidal (if is greater than or equal to ).

  `tau` is a matrix of the same type as `A` and of length min{, }. On exit, is stored in the lower triangular/trapezoidal part of `A`. The matrix is stored as a product of min{, } elementary reflectors in the first min{, } rows of `A` and in `tau`.

cvxopt.lapack.geqp3(A, jpvt, tau)

  QR factorization with column pivoting of a real or complex matrix :

  If is by , then is by and orthogonal/unitary, and is by and upper triangular (if is greater than or equal to ), or upper trapezoidal (if is less than or equal to ).

  `tau` is a matrix of the same type as `A` and of length min{, }. `jpvt` is an integer matrix of length . On entry, if `jpvt[k]` is nonzero, then column of is permuted to the front of . Otherwise, column is a free column.

  On exit, `jpvt` contains the permutation : the operation is equivalent to `A[:, jpvt-1]`. is stored in the upper triangular/trapezoidal part of `A`. The matrix is stored as a product of min{, } elementary reflectors in the first min{,:math:n} columns of `A` and in `tau`.

In most applications, the matrix is not needed explicitly, and it is sufficient to be able to make products with or its transpose. The functions `unmqr` and `ormqr` multiply a matrix with the orthogonal matrix computed by `geqrf`.

cvxopt.lapack.unmqr(A, tau, C[, side = 'L', trans = 'N'])

  Product with a real orthogonal or complex unitary matrix:

  where

  If `A` is by , then is square of order and orthogonal or unitary. is stored in the first min{, } columns of `A` and in `tau` as a product of min{, } elementary reflectors, as computed by `geqrf`. The matrices `A`, `tau`, and `C` must have the same type. `trans` = `'T'` is only allowed if the typecode is `'d'`.

cvxopt.lapack.ormqr(A, tau, C[, side = 'L', trans = 'N'])

  Identical to `unmqr` but works only for real matrices, and the possible values of `trans` are `'N'` and `'T'`.

As an example, we solve a least-squares problem by a direct call to `gels`, and by separate calls to `geqrf`, `ormqr`, and `trtrs`.

    >>> from cvxopt import blas, lapack, matrix, normal
    >>> m, n = 10, 5
    >>> A, b = normal(m,n), normal(m,1)
    >>> x1 = +b
    >>> lapack.gels(+A, x1)                  # x1[:n] minimizes || A*x - b ||_2
    >>> tau = matrix(0.0, (n,1))
    >>> lapack.geqrf(A, tau)                 # A = [Q1, Q2] * [R1; 0]
    >>> x2 = +b
    >>> lapack.ormqr(A, tau, x2, trans='T')  # x2 := [Q1, Q2]' * x2
    >>> lapack.trtrs(A[:n,:], x2, uplo='U')  # x2[:n] := R1^{-1} * x2[:n]
    >>> blas.nrm2(x1[:n] - x2[:n])
    3.0050798580569307e-16

The next two functions make products with the orthogonal matrix computed by `gelqf`.

cvxopt.lapack.unmlq(A, tau, C[, side = 'L', trans = 'N'])

  Product with a real orthogonal or complex unitary matrix:

  where

  If `A` is by , then is square of order and orthogonal or unitary. is stored in the first min{, } rows of `A` and in `tau` as a product of min{, } elementary reflectors, as computed by `gelqf`. The matrices `A`, `tau`, and `C` must have the same type. `trans` = `'T'` is only allowed if the typecode is `'d'`.

cvxopt.lapack.ormlq(A, tau, C[, side = 'L', trans = 'N'])

  Identical to `unmlq` but works only for real matrices, and the possible values of `trans` or `'N'` and `'T'`.

As an example, we solve a least-norm problem by a direct call to `gels`, and by separate calls to `gelqf`, `ormlq`, and `trtrs`.

    >>> from cvxopt import blas, lapack, matrix, normal
    >>> m, n = 5, 10
    >>> A, b = normal(m,n), normal(m,1)
    >>> x1 = matrix(0.0, (n,1))
    >>> x1[:m] = b
    >>> lapack.gels(+A, x1)                  # x1 minimizes ||x||_2 subject to A*x = b
    >>> tau = matrix(0.0, (m,1))
    >>> lapack.gelqf(A, tau)                 # A = [L1, 0] * [Q1; Q2]
    >>> x2 = matrix(0.0, (n,1))
    >>> x2[:m] = b                           # x2 = [b; 0]
    >>> lapack.trtrs(A[:,:m], x2)            # x2[:m] := L1^{-1} * x2[:m]
    >>> lapack.ormlq(A, tau, x2, trans='T')  # x2 := [Q1, Q2]' * x2
    >>> blas.nrm2(x1 - x2)
    0.0

Finally, if the matrix is needed explicitly, it can be generated from the output of `geqrf` and `gelqf` using one of the following functions.

cvxopt.lapack.ungqr(A, tau)

  If `A` has size by , and `tau` has length , then, on entry, the first `k` columns of the matrix `A` and the entries of `tau` contai an unitary or orthogonal matrix of order , as computed by `geqrf`. On exit, the first min{, } columns of are contained in the leading columns of `A`.

cvxopt.lapack.orgqr(A, tau)

  Identical to `ungqr` but works only for real matrices.

cvxopt.lapack.unglq(A, tau)

  If `A` has size by , and `tau` has length , then, on entry, the first `k` rows of the matrix `A` and the entries of `tau` contain a unitary or orthogonal matrix of order , as computed by `gelqf`. On exit, the first min{, } rows of are contained in the leading rows of `A`.

cvxopt.lapack.orglq(A, tau)

  Identical to `unglq` but works only for real matrices.

We illustrate this with the QR factorization of the matrix

    >>> from cvxopt import matrix, lapack
    >>> A = matrix([ [6., 6., 19., 6.], [-5., 3., -2., -10.], [4., -4., 7., -5] ])
    >>> m, n = A.size
    >>> tau = matrix(0.0, (n,1))
    >>> lapack.geqrf(A, tau)
    >>> print(A[:n, :])              # Upper triangular part is R.
    [-2.17e+01  5.08e+00 -4.76e+00]
    [ 2.17e-01 -1.06e+01 -2.66e+00]
    [ 6.87e-01  3.12e-01 -8.74e+00]
    >>> Q1 = +A
    >>> lapack.orgqr(Q1, tau)
    >>> print(Q1)
    [-2.77e-01  3.39e-01 -4.10e-01]
    [-2.77e-01 -4.16e-01  7.35e-01]
    [-8.77e-01 -2.32e-01 -2.53e-01]
    [-2.77e-01  8.11e-01  4.76e-01]
    >>> Q = matrix(0.0, (m,m))
    >>> Q[:, :n] = A
    >>> lapack.orgqr(Q, tau)
    >>> print(Q)                     # Q = [ Q1, Q2]
    [-2.77e-01  3.39e-01 -4.10e-01 -8.00e-01]
    [-2.77e-01 -4.16e-01  7.35e-01 -4.58e-01]
    [-8.77e-01 -2.32e-01 -2.53e-01  3.35e-01]
    [-2.77e-01  8.11e-01  4.76e-01  1.96e-01]

The orthogonal matrix in the factorization

can be generated as follows.

    >>> A = matrix([ [3., -2., 9.], [-16., -12., 19.], [-10., -3., 6.], [-1., 4., -6.] ])
    >>> m, n = A.size
    >>> tau = matrix(0.0, (m,1))
    >>> lapack.geqrf(A, tau)
    >>> R = +A
    >>> print(R)                     # Upper trapezoidal part is [R1, R2].
    [-9.70e+00 -1.52e+01 -3.09e+00  6.70e+00]
    [-1.58e-01  2.30e+01  1.14e+01 -1.92e+00]
    [ 7.09e-01 -5.57e-01  2.26e+00  2.09e+00]
    >>> lapack.orgqr(A, tau)
    >>> print(A[:, :m])              # Q is in the first m columns of A.
    [-3.09e-01 -8.98e-01 -3.13e-01]
    [ 2.06e-01 -3.85e-01  9.00e-01]
    [-9.28e-01  2.14e-01  3.04e-01]

## Symmetric and Hermitian Eigenvalue Decomposition

The first four routines compute all or selected eigenvalues and eigenvectors of a real symmetric matrix :

cvxopt.lapack.syev(A, W[, jobz = 'N', uplo = 'L'])

  Eigenvalue decomposition of a real symmetric matrix of order .

  `W` is a real matrix of length at least . On exit, `W` contains the eigenvalues in ascending order. If `jobz` is `'V'`, the eigenvectors are also computed and returned in `A`. If `jobz` is `'N'`, the eigenvectors are not returned and the contents of `A` are destroyed.

  Raises an `ArithmeticError` if the eigenvalue decomposition fails.

cvxopt.lapack.syevd(A, W[, jobz = 'N', uplo = 'L'])

  This is an alternative to `syev`, based on a different algorithm. It is faster on large problems, but also uses more memory.

cvxopt.lapack.syevx(A, W[, jobz = 'N', range = 'A', uplo = 'L', vl = 0.0, vu = 0.0, il = 1, iu = 1, Z = None])

  Computes selected eigenvalues and eigenvectors of a real symmetric matrix of order .

  `W` is a real matrix of length at least . On exit, `W` contains the eigenvalues in ascending order. If `range` is `'A'`, all the eigenvalues are computed. If `range` is `'I'`, eigenvalues through are computed, where . If `range` is `'V'`, the eigenvalues in the interval are computed.

  If `jobz` is `'V'`, the (normalized) eigenvectors are computed, and returned in `Z`. If `jobz` is `'N'`, the eigenvectors are not computed. In both cases, the contents of `A` are destroyed on exit.

  `Z` is optional (and not referenced) if `jobz` is `'N'`. It is required if `jobz` is `'V'` and must have at least columns if `range` is `'A'` or `'V'` and at least columns if `range` is `'I'`.

  `syevx` returns the number of computed eigenvalues.

cvxopt.lapack.syevr(A, W[, jobz = 'N', range = 'A', uplo = 'L', vl = 0.0, vu = 0.0, il = 1, iu = n, Z = None])

  This is an alternative to `syevx`. `syevr` is the most recent LAPACK routine for symmetric eigenvalue problems, and expected to supersede the three other routines in future releases.

The next four routines can be used to compute eigenvalues and eigenvectors for complex Hermitian matrices:

For real symmetric matrices they are identical to the corresponding `syev*` routines.

cvxopt.lapack.heev(A, W[, jobz = 'N', uplo = 'L'])

  Eigenvalue decomposition of a real symmetric or complex Hermitian matrix of order .

  The calling sequence is identical to `syev`, except that `A` can be real or complex.

cvxopt.lapack.heevd(A, W[, jobz = 'N'[, uplo = 'L']])

  This is an alternative to `heev`.

cvxopt.lapack.heevx(A, W[, jobz = 'N', range = 'A', uplo = 'L', vl = 0.0, vu = 0.0, il = 1, iu = n, Z = None])

  Computes selected eigenvalues and eigenvectors of a real symmetric or complex Hermitian matrix.

  The calling sequence is identical to `syevx`, except that `A` can be real or complex. `Z` must have the same type as `A`.

cvxopt.lapack.heevr(A, W[, jobz = 'N', range = 'A', uplo = 'L', vl = 0.0, vu = 0.0, il = 1, iu = n, Z = None])

  This is an alternative to `heevx`.

## Generalized Symmetric Definite Eigenproblems

Three types of generalized eigenvalue problems can be solved:

(2)

with and real symmetric or complex Hermitian, and is positive definite. The matrix of eigenvectors is normalized as follows:

cvxopt.lapack.sygv(A, B, W[, itype = 1, jobz = 'N', uplo = 'L'])

  Solves the generalized eigenproblem (2) for real symmetric matrices of order , stored in real matrices `A` and `B`. `itype` is an integer with possible values 1, 2, 3, and specifies the type of eigenproblem. `W` is a real matrix of length at least . On exit, it contains the eigenvalues in ascending order. On exit, `B` contains the Cholesky factor of . If `jobz` is `'V'`, the eigenvectors are computed and returned in `A`. If `jobz` is `'N'`, the eigenvectors are not returned and the contents of `A` are destroyed.

cvxopt.lapack.hegv(A, B, W[, itype = 1, jobz = 'N', uplo = 'L'])

  Generalized eigenvalue problem (2) of real symmetric or complex Hermitian matrix of order . The calling sequence is identical to `sygv`, except that `A` and `B` can be real or complex.

## Singular Value Decomposition

cvxopt.lapack.gesvd(A, S[, jobu = 'N', jobvt = 'N', U = None, Vt = None])

  Singular value decomposition

  of a real or complex by matrix .

  `S` is a real matrix of length at least min{, }. On exit, its first min{, } elements are the singular values in descending order.

  The argument `jobu` controls how many left singular vectors are computed. The possible values are `'N'`, `'A'`, `'S'` and `'O'`. If `jobu` is `'N'`, no left singular vectors are computed. If `jobu` is `'A'`, all left singular vectors are computed and returned as columns of `U`. If `jobu` is `'S'`, the first min{, } left singular vectors are computed and returned as columns of `U`. If `jobu` is `'O'`, the first min{, } left singular vectors are computed and returned as columns of `A`. The argument `U` is None(if `jobu` is `'N'` or `'A'`) or a matrix of the same type as `A`.

  The argument `jobvt` controls how many right singular vectors are computed. The possible values are `'N'`, `'A'`, `'S'` and `'O'`. If `jobvt` is `'N'`, no right singular vectors are computed. If `jobvt` is `'A'`, all right singular vectors are computed and returned as rows of `Vt`. If `jobvt` is `'S'`, the first min{, } right singular vectors are computed and their (conjugate) transposes are returned as rows of `Vt`. If `jobvt` is `'O'`, the first min{, } right singular vectors are computed and their (conjugate) transposes are returned as rows of `A`. Note that the (conjugate) transposes of the right singular vectors (i.e., the matrix ) are returned in `Vt` or `A`. The argument `Vt` can be `None` (if `jobvt` is `'N'` or `'A'`) or a matrix of the same type as `A`.

  On exit, the contents of `A` are destroyed.

cvxopt.lapack.gesdd(A, S[, jobz = 'N', U = None, Vt = None])

  Singular value decomposition of a real or complex by matrix.. This function is based on a divide-and-conquer algorithm and is faster than `gesvd`.

  `S` is a real matrix of length at least min{, }. On exit, its first min{, } elements are the singular values in descending order.

  The argument `jobz` controls how many singular vectors are computed. The possible values are `'N'`, `'A'`, `'S'` and `'O'`. If `jobz` is `'N'`, no singular vectors are computed. If `jobz` is `'A'`, all left singular vectors are computed and returned as columns of `U` and all right singular vectors are computed and returned as rows of `Vt`. If `jobz` is `'S'`, the first min{, } left and right singular vectors are computed and returned as columns of `U` and rows of `Vt`. If `jobz` is `'O'` and is greater than or equal to , the first left singular vectors are returned as columns of `A` and the right singular vectors are returned as rows of `Vt`. If `jobz` is `'O'` and is less than , the left singular vectors are returned as columns of `U` and the first right singular vectors are returned as rows of `A`. Note that the (conjugate) transposes of the right singular vectors are returned in `Vt` or `A`.

  The argument `U` can be `None` (if `jobz` is `'N'` or `'A'` of `jobz` is `'O'` and is greater than or equal to ) or a matrix of the same type as `A`. The argument `Vt` can be None(if `jobz` is `'N'` or `'A'` or `jobz` is `'O'` and :math`m` is less than ) or a matrix of the same type as `A`.

  On exit, the contents of `A` are destroyed.

## Schur and Generalized Schur Factorization

cvxopt.lapack.gees(A[, w = None, V = None, select = None])

  Computes the Schur factorization

  of a real or complex by matrix .

  If is real, the matrix of Schur vectors is orthogonal, and is a real upper quasi-triangular matrix with 1 by 1 or 2 by 2 diagonal blocks. The 2 by 2 blocks correspond to complex conjugate pairs of eigenvalues of . If is complex, the matrix of Schur vectors is unitary, and is a complex upper triangular matrix with the eigenvalues of on the diagonal.

  The optional argument `w` is a complex matrix of length at least . If it is provided, the eigenvalues of `A` are returned in `w`. The optional argument `V` is an by matrix of the same type as `A`. If it is provided, then the Schur vectors are returned in `V`.

  The argument `select` is an optional ordering routine. It must be a Python function that can be called as `f(s)` with a complex argument `s`, and returns `True` or `False`. The eigenvalues for which `select` returns `True` will be selected to appear first along the diagonal. (In the real Schur factorization, if either one of a complex conjugate pair of eigenvalues is selected, then both are selected.)

  On exit, `A` is replaced with the matrix . The function `gees` returns an integer equal to the number of eigenvalues that were selected by the ordering routine. If `select` is `None`, then `gees` returns 0.

As an example we compute the complex Schur form of the matrix

    >>> A = matrix([[-7., 5., 11., -4., 13.], [-11., -3., 11., 8., -19.], [-6., 3., -5., 0., -12.],
                    [-4., -12., -14., 8., -8.], [11., 0., 9., 6., 10.]])
    >>> S = matrix(A, tc='z')
    >>> w = matrix(0.0, (5,1), 'z')
    >>> lapack.gees(S, w)
    0
    >>> print(S)
    [ 5.67e+00+j1.69e+01 -2.13e+01+j2.85e+00  1.40e+00+j5.88e+00 -4.19e+00+j2.05e-01  3.19e+00-j1.01e+01]
    [ 0.00e+00-j0.00e+00  5.67e+00-j1.69e+01  1.09e+01+j5.93e-01 -3.29e+00-j1.26e+00 -1.26e+01+j7.80e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  1.27e+01+j3.43e-17 -6.83e+00+j2.18e+00  5.31e+00-j1.69e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00 -1.31e+01-j0.00e+00 -2.60e-01-j0.00e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00 -7.86e+00-j0.00e+00]
    >>> print(w)
    [ 5.67e+00+j1.69e+01]
    [ 5.67e+00-j1.69e+01]
    [ 1.27e+01+j3.43e-17]
    [-1.31e+01-j0.00e+00]
    [-7.86e+00-j0.00e+00]

An ordered Schur factorization with the eigenvalues in the left half of the complex plane ordered first, can be computed as follows.

    >>> S = matrix(A, tc='z')
    >>> def F(x): return (x.real < 0.0)
    ...
    >>> lapack.gees(S, w, select = F)
    2
    >>> print(S)
    [-1.31e+01-j0.00e+00 -1.72e-01+j7.93e-02 -2.81e+00+j1.46e+00  3.79e+00-j2.67e-01  5.14e+00-j4.84e+00]
    [ 0.00e+00-j0.00e+00 -7.86e+00-j0.00e+00 -1.43e+01+j8.31e+00  5.17e+00+j8.79e+00  2.35e+00-j7.86e-01]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  5.67e+00+j1.69e+01 -1.71e+01-j1.41e+01  1.83e+00-j4.63e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  5.67e+00-j1.69e+01 -8.75e+00+j2.88e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  1.27e+01+j3.43e-17]
    >>> print(w)
    [-1.31e+01-j0.00e+00]
    [-7.86e+00-j0.00e+00]
    [ 5.67e+00+j1.69e+01]
    [ 5.67e+00-j1.69e+01]
    [ 1.27e+01+j3.43e-17]

cvxopt.lapack.gges(A, B[, a = None, b = None, Vl = None, Vr = None, select = None])

  Computes the generalized Schur factorization

  of a pair of real or complex by matrices , .

  If and are real, then the matrices of left and right Schur vectors and are orthogonal, is a real upper quasi-triangular matrix with 1 by 1 or 2 by 2 diagonal blocks, and is a real triangular matrix with nonnegative diagonal. The 2 by 2 blocks along the diagonal of correspond to complex conjugate pairs of generalized eigenvalues of , . If and are complex, the matrices of left and right Schur vectors and are unitary, is complex upper triangular, and is complex upper triangular with nonnegative real diagonal.

  The optional arguments `a` and `b` are `'z'` and `'d'` matrices of length at least . If these are provided, the generalized eigenvalues of `A`, `B` are returned in `a` and `b`. (The generalized eigenvalues are the ratios `a[k] / b[k]`.) The optional arguments `Vl` and `Vr` are by matrices of the same type as `A` and `B`. If they are provided, then the left Schur vectors are returned in `Vl` and the right Schur vectors are returned in `Vr`.

  The argument `select` is an optional ordering routine. It must be a Python function that can be called as `f(x,y)` with a complex argument `x` and a real argument `y`, and returns `True` or `False`. The eigenvalues for which `select` returns `True` will be selected to appear first on the diagonal. (In the real Schur factorization, if either one of a complex conjugate pair of eigenvalues is selected, then both are selected.)

  On exit, `A` is replaced with the matrix and `B` is replaced with the matrix . The function `gges` returns an integer equal to the number of eigenvalues that were selected by the ordering routine. If `select` is `None`, then `gges` returns 0.

As an example, we compute the generalized complex Schur form of the matrix of the previous example, and

    >>> A = matrix([[-7., 5., 11., -4., 13.], [-11., -3., 11., 8., -19.], [-6., 3., -5., 0., -12.],
                    [-4., -12., -14., 8., -8.], [11., 0., 9., 6., 10.]])
    >>> B = matrix(0.0, (5,5))
    >>> B[:19:6] = 1.0
    >>> S = matrix(A, tc='z')
    >>> T = matrix(B, tc='z')
    >>> a = matrix(0.0, (5,1), 'z')
    >>> b = matrix(0.0, (5,1))
    >>> lapack.gges(S, T, a, b)
    0
    >>> print(S)
    [ 6.64e+00-j8.87e+00 -7.81e+00-j7.53e+00  6.16e+00-j8.51e-01  1.18e+00+j9.17e+00  5.88e+00-j4.51e+00]
    [ 0.00e+00-j0.00e+00  8.48e+00+j1.13e+01 -2.12e-01+j1.00e+01  5.68e+00+j2.40e+00 -2.47e+00+j9.38e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00 -1.39e+01-j0.00e+00  6.78e+00-j0.00e+00  1.09e+01-j0.00e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00 -6.62e+00-j0.00e+00 -2.28e-01-j0.00e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00 -2.89e+01-j0.00e+00]
    >>> print(T)
    [ 6.46e-01-j0.00e+00  4.29e-01-j4.79e-02  2.02e-01-j3.71e-01  1.08e-01-j1.98e-01 -1.95e-01+j3.58e-01]
    [ 0.00e+00-j0.00e+00  8.25e-01-j0.00e+00 -2.17e-01+j3.11e-01 -1.16e-01+j1.67e-01  2.10e-01-j3.01e-01]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  7.41e-01-j0.00e+00 -3.25e-01-j0.00e+00  5.87e-01-j0.00e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  8.75e-01-j0.00e+00  4.84e-01-j0.00e+00]
    [ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00  0.00e+00-j0.00e+00]
    >>> print(a)
    [ 6.64e+00-j8.87e+00]
    [ 8.48e+00+j1.13e+01]
    [-1.39e+01-j0.00e+00]
    [-6.62e+00-j0.00e+00]
    [-2.89e+01-j0.00e+00]
    >>> print(b)
    [ 6.46e-01]
    [ 8.25e-01]
    [ 7.41e-01]
    [ 8.75e-01]
    [ 0.00e+00]

## Example: Analytic Centering

The analytic centering problem is defined as

In the code below we solve the problem using Newton’s method. At each iteration the Newton direction is computed by solving a positive definite set of linear equations

(where has rows ), and a suitable step size is determined by a backtracking line search.

We use the level-3 BLAS function `blas.syrk` to form the Hessian matrix and the LAPACK function `posv` to solve the Newton system. The code can be further optimized by replacing the matrix-vector products with the level-2 BLAS function `blas.gemv`.

    from cvxopt import matrix, log, mul, div, blas, lapack
    from math import sqrt

    def acent(A,b):
        """
        Returns the analytic center of A*x <= b.
        We assume that b > 0 and the feasible set is bounded.
        """

        MAXITERS = 100
        ALPHA = 0.01
        BETA = 0.5
        TOL = 1e-8

        m, n = A.size
        x = matrix(0.0, (n,1))
        H = matrix(0.0, (n,n))

        for iter in xrange(MAXITERS):

            # Gradient is g = A^T * (1./(b-A*x)).
            d = (b - A*x)**-1
            g = A.T * d

            # Hessian is H = A^T * diag(d)^2 * A.
            Asc = mul( d[:,n*[0]], A )
            blas.syrk(Asc, H, trans='T')

            # Newton step is v = -H^-1 * g.
            v = -g
            lapack.posv(H, v)

            # Terminate if Newton decrement is less than TOL.
            lam = blas.dot(g, v)
            if sqrt(-lam) < TOL: return x

            # Backtracking line search.
            y = mul(A*v, d)
            step = 1.0
            while 1-step*max(y) < 0: step *= BETA
            while True:
                if -sum(log(1-step*y)) < ALPHA*step*lam: break
                step *= BETA
            x += step*v