Eigensystems¶ ↑

Contentes¶ ↑

1. Modules and classes

2. Real Symmetric Matrices

3. Complex Hermitian Matrices

4. Real Nonsymmetric Matrices (>= GSL-1.9)

5. Real Generalized Symmetric-Definite Eigensystems (>= GSL-1.10)

6. Complex Generalized Hermitian-Definite Eigensystems (>= GSL-1.10)

7. Real Generalized Nonsymmetric Eigensystems (>= GSL-1.10)

8. Sorting Eigenvalues and Eigenvectors

Modules and classes¶ ↑

• GSL

  • Eigen

    • EigenValues < Vector

    • EigenVectors < Matrix

    • Symm (Module)

      • Workspace (Class)

    • Symmv (Module)

      • Workspace (Class)

    • Nonsymm (Module, >= GSL-1.9)

      • Workspace (Class)

    • Nonsymmv (Module, >= GSL-1.9)

      • Workspace (Class)

    • Gensymm (Module, >= GSL-1.10)

      • Workspace (Class)

    • Gensymmv (Module, >= GSL-1.10)

      • Workspace (Class)

    • Herm (Module)

      • Workspace (Class)

    • Hermv (Module)

      • Workspace (Class)

      • Vectors < Matrix::Complex

    • Genherm (Module, >= GSL-1.10)

      • Workspace (Class)

    • Genhermv (Module, >= GSL-1.10)

      • Workspace (Class)

    • Gen (Module, >= GSL-1.10)

      • Workspace (Class)

    • Genv (Module, >= GSL-1.10)

      • Workspace (Class)

Real Symmetric Matrices¶ ↑

Workspace classes¶ ↑

• GSL::Eigen::Symm::Workspace.alloc(n)

• GSL::Eigen::Symmv::Workspace.alloc(n)

• GSL::Eigen::Herm::Workspace.alloc(n)

• GSL::Eigen::Hermv::Workspace.alloc(n)

Methods to solve eigensystems¶ ↑

• GSL::Eigen::symm(A)

• GSL::Eigen::symm(A, workspace)

• GSL::Matrix#eigen_symm

• GSL::Matrix#eigen_symm(workspace)

  These methods compute the eigenvalues of the real symmetric matrix. The workspace object workspace can be omitted.

• GSL::Eigen::symmv(A)

• GSL::Matrix#eigen_symmv

  These methods compute the eigenvalues and eigenvectors of the real symmetric matrix, and return an array of two elements: The first is a GSL::Vector object which stores all the eigenvalues. The second is a GSL::Matrix object, whose columns contain eigenvectors.

1. Singleton method of the GSL::Eigen module, GSL::Eigen::symm

   m = GSL::Matrix.alloc([1.0, 1/2.0, 1/3.0, 1/4.0], [1/2.0, 1/3.0, 1/4.0, 1/5.0],
                      [1/3.0, 1/4.0, 1/5.0, 1/6.0], [1/4.0, 1/5.0, 1/6.0, 1/7.0])
   eigval, eigvec = Eigen::symmv(m)
   

2. Instance method of GSL::Matrix class

   eigval, eigvec = m.eigen_symmv
   

Complex Hermitian Matrices¶ ↑

• GSL::Eigen::herm(A)

• GSL::Eigen::herm(A, workspace)

• GSL::Matrix::Complex#eigen_herm

• GSL::Matrix::Complex#eigen_herm(workspace)

  These methods compute the eigenvalues of the complex hermitian matrix.

• GSL::Eigen::hermv(A)

• GSL::Eigen::hermv(A, workspace)

• GSL::Matrix::Complex#eigen_hermv

• GSL::Matrix::Complex#eigen_hermv(workspace

Real Nonsymmetric Matrices (>= GSL-1.9)¶ ↑

• GSL::Eigen::Nonsymm.alloc(n)

  This allocates a workspace for computing eigenvalues of n-by-n real nonsymmetric matrices. The size of the workspace is O(2n).

• GSL::Eigen::Nonsymm::params(compute_t, balance, wspace)

• GSL::Eigen::Nonsymm::Workspace#params(compute_t, balance)

  This method sets some parameters which determine how the eigenvalue problem is solved in subsequent calls to GSL::Eigen::nonsymm. If compute_t is set to 1, the full Schur form T will be computed by gsl_eigen_nonsymm. If it is set to 0, T will not be computed (this is the default setting). Computing the full Schur form T requires approximately 1.5-2 times the number of flops.

  If balance is set to 1, a balancing transformation is applied to the matrix prior to computing eigenvalues. This transformation is designed to make the rows and columns of the matrix have comparable norms, and can result in more accurate eigenvalues for matrices whose entries vary widely in magnitude. See section Balancing for more information. Note that the balancing transformation does not preserve the orthogonality of the Schur vectors, so if you wish to compute the Schur vectors with GSL::Eigen::nonsymm_Z you will obtain the Schur vectors of the balanced matrix instead of the original matrix. The relationship will be where Q is the matrix of Schur vectors for the balanced matrix, and D is the balancing transformation. Then GSL::Eigen::nonsymm_Z will compute a matrix Z which satisfies with Z = D Q. Note that Z will not be orthogonal. For this reason, balancing is not performed by default.

• GSL::Eigen::nonsymm(m, eval, wspace)

• GSL::Eigen::nonsymm(m)

• GSL::Matrix#eigen_nonsymm()

• GSL::Matrix#eigen_nonsymm(wspace)

• GSL::Matrix#eigen_nonsymm(eval, wspace)

  These methods compute the eigenvalues of the real nonsymmetric matrix m and return them, or store in the vector eval if it given. If T is desired, it is stored in m on output, however the lower triangular portion will not be zeroed out. Otherwise, on output, the diagonal of m will contain the 1-by-1 real eigenvalues and 2-by-2 complex conjugate eigenvalue systems, and the rest of m is destroyed.

• GSL::Eigen::nonsymm_Z(m, eval, Z, wspace)

• GSL::Eigen::nonsymm_Z(m)

• GSL::Matrix#eigen_nonsymm_Z()

• GSL::Matrix#eigen_nonsymm(eval, Z, wspace)

  These methods are identical to GSL::Eigen::nonsymm except they also compute the Schur vectors and return them (or store into Z).

• GSL::Eigen::Nonsymmv.alloc(n)

  Allocates a workspace for computing eigenvalues and eigenvectors of n-by-n real nonsymmetric matrices. The size of the workspace is O(5n).

• GSL::Eigen::nonsymm(m)

• GSL::Eigen::nonsymm(m, wspace)

• GSL::Eigen::nonsymm(m, eval, evec)

• GSL::Eigen::nonsymm(m, eval, evec, wspace)

• GSL::Matrix#eigen_nonsymmv()

• GSL::Matrix#eigen_nonsymmv(wspace)

• GSL::Matrix#eigen_nonsymmv(eval, evec)

• GSL::Matrix#eigen_nonsymmv(eval, evec, wspace)

  Compute eigenvalues and right eigenvectors of the n-by-n real nonsymmetric matrix. The computed eigenvectors are normalized to have Euclidean norm 1. On output, the upper portion of m contains the Schur form T.

Real Generalized Symmetric-Definite Eigensystems (GSL-1.10)¶ ↑

The real generalized symmetric-definite eigenvalue problem is to find eigenvalues lambda and eigenvectors x such that where A and B are symmetric matrices, and B is positive-definite. This problem reduces to the standard symmetric eigenvalue problem by applying the Cholesky decomposition to B: Therefore, the problem becomes C y = lambda y where C = L^{-1} A L^{-t} is symmetric, and y = L^t x. The standard symmetric eigensolver can be applied to the matrix C. The resulting eigenvectors are backtransformed to find the vectors of the original problem. The eigenvalues and eigenvectors of the generalized symmetric-definite eigenproblem are always real.

• GSL::Eigen::Gensymm.alloc(n)

• GSL::Eigen::Gensymm::Workspace.alloc(n)

  Allocates a workspace for computing eigenvalues of n-by-n real generalized symmetric-definite eigensystems. The size of the workspace is O(2n).

• GSL::Eigen::gensymm(A, B, w)

  Computes the eigenvalues of the real generalized symmetric-definite matrix pair A, B, and returns them as a GSL::Vector, using the method outlined above. On output, B contains its Cholesky decomposition.

• GSL::Eigen::gensymmv(A, B, w)

  Computes the eigenvalues and eigenvectors of the real generalized symmetric-definite matrix pair A, B, and returns them as a GSL::Vector and a GSL::Matrix. The computed eigenvectors are normalized to have unit magnitude. On output, B contains its Cholesky decomposition.

Complex Generalized Hermitian-Definite Eigensystems (>= GSL-1.10)¶ ↑

The complex generalized hermitian-definite eigenvalue problem is to find eigenvalues lambda and eigenvectors x such that where A and B are hermitian matrices, and B is positive-definite. Similarly to the real case, this can be reduced to C y = lambda y where C = L^{-1} A L^{-H} is hermitian, and y = L^H x. The standard hermitian eigensolver can be applied to the matrix C. The resulting eigenvectors are backtransformed to find the vectors of the original problem. The eigenvalues of the generalized hermitian-definite eigenproblem are always real.

• GSL::Eigen::Genherm.alloc(n)

  Allocates a workspace for computing eigenvalues of n-by-n complex generalized hermitian-definite eigensystems. The size of the workspace is O(3n).

• GSL::Eigen::genherm(A, B, w)

  Computes the eigenvalues of the complex generalized hermitian-definite matrix pair A, B, and returns them as a GSL::Vector, using the method outlined above. On output, B contains its Cholesky decomposition.

• GSL::Eigen::genherm(A, B, w)

  Computes the eigenvalues and eigenvectors of the complex generalized hermitian-definite matrix pair A, B, and returns them as a GSL::Vector and a GSL::Matrix::Complex. The computed eigenvectors are normalized to have unit magnitude. On output, B contains its Cholesky decomposition.

Real Generalized Nonsymmetric Eigensystems (>= GSL-1.10)¶ ↑

• GSL::Eigen::Gen.alloc(n)

• GSL::Eigen::Gen::Workspace.alloc(n)

  Allocates a workspace for computing eigenvalues of n-by-n real generalized nonsymmetric eigensystems. The size of the workspace is O(n).

• GSL::Eigen::Gen::params(compute_s, compute_t, balance, w)

• GSL::Eigen::gen_params(compute_s, compute_t, balance, w)

  Set some parameters which determine how the eigenvalue problem is solved in subsequent calls to GSL::Eigen::gen.

  If compute_s is set to 1, the full Schur form S will be computed by GSL::Eigen::gen. If it is set to 0, S will not be computed (this is the default setting). S is a quasi upper triangular matrix with 1-by-1 and 2-by-2 blocks on its diagonal. 1-by-1 blocks correspond to real eigenvalues, and 2-by-2 blocks correspond to complex eigenvalues.

  If compute_t is set to 1, the full Schur form T will be computed by GSL::Eigen::gen. If it is set to 0, T will not be computed (this is the default setting). T is an upper triangular matrix with non-negative elements on its diagonal. Any 2-by-2 blocks in S will correspond to a 2-by-2 diagonal block in T.

  The balance parameter is currently ignored, since generalized balancing is not yet implemented.

• GSL::Eigen::gen(A, B, w)

  Computes the eigenvalues of the real generalized nonsymmetric matrix pair A, B, and returns them as pairs in (alpha, beta), where alpha is GSL::Vector::Complex and beta is GSL::Vector. If beta_i is non-zero, then lambda = alpha_i / beta_i is an eigenvalue. Likewise, if alpha_i is non-zero, then mu = beta_i / alpha_i is an eigenvalue of the alternate problem mu A y = B y. The elements of beta are normalized to be non-negative.

  If S is desired, it is stored in A on output. If T is desired, it is stored in B on output. The ordering of eigenvalues in alpha, beta follows the ordering of the diagonal blocks in the Schur forms S and T.

• GSL::Eigen::gen_QZ(A, B, w)

  This method is identical to GSL::Eigen::gen except it also computes the left and right Schur vectors and returns them.

• GSL::Eigen::Genv.alloc(n)

• GSL::Eigen::Genv::Workspace.alloc(n)

  Allocatesa workspace for computing eigenvalues and eigenvectors of n-by-n real generalized nonsymmetric eigensystems. The size of the workspace is O(7n).

• GSL::Eigen::genv(A, B, w)

  Computes eigenvalues and right eigenvectors of the n-by-n real generalized nonsymmetric matrix pair A, B. The eigenvalues and eigenvectors are returned in alpha, beta, evec. On output, A, B contains the generalized Schur form S, T.

• GSL::Eigen::genv_QZ(A, B, w)

  This method is identical to GSL::Eigen::genv except it also computes the left and right Schur vectors and returns them.

Sorting Eigenvalues and Eigenvectors¶ ↑

• GSL::Eigen::symmv_sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

• GSL::Eigen::Symmv::sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

  These methods simultaneously sort the eigenvalues stored in the vector eval and the corresponding real eigenvectors stored in the columns of the matrix evec into ascending or descending order according to the value of the parameter type,

  • GSL::Eigen::SORT_VAL_ASC ascending order in numerical value

  • GSL::Eigen::SORT_VAL_DESC escending order in numerical value

  • GSL::Eigen::SORT_ABS_ASC scending order in magnitude

  • GSL::Eigen::SORT_ABS_DESC descending order in magnitude

  The sorting is carried out in-place!

• GSL::Eigen::hermv_sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

• GSL::Eigen::Hermv::sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

  These methods simultaneously sort the eigenvalues stored in the vector eval and the corresponding complex eigenvectors stored in the columns of the matrix evec into ascending or descending order according to the value of the parameter type as shown above.

• GSL::Eigen::nonsymmv_sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

• GSL::Eigen::Nonsymmv::sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

  Sorts the eigenvalues stored in the vector eval and the corresponding complex eigenvectors stored in the columns of the matrix evec into ascending or descending order according to the value of the parameter type as shown above. Only GSL::EIGEN_SORT_ABS_ASC and GSL::EIGEN_SORT_ABS_DESC are supported due to the eigenvalues being complex.

• GSL::Eigen::gensymmv_sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

• GSL::Eigen::Gensymmv::sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

  Sorts the eigenvalues stored in the vector eval and the corresponding real eigenvectors stored in the columns of the matrix evec into ascending or descending order according to the value of the parameter type as shown above.

• GSL::Eigen::gensymmv_sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

• GSL::Eigen::Gensymmv::sort(eval, evec, type = GSL::Eigen::SORT_VAL_ASC)

  Sorts the eigenvalues stored in the vector eval and the corresponding complex eigenvectors stored in the columns of the matrix evec into ascending or descending order according to the value of the parameter type as shown above.

• GSL::Eigen::genv_sort(alpha, beta, evec, type = GSL::Eigen::SORT_VAL_ASC)

• GSL::Eigen::Genv::sort(alpha, beta, evec, type = GSL::Eigen::SORT_VAL_ASC)

  Sorts the eigenvalues stored in the vectors alpha, beta and the corresponding complex eigenvectors stored in the columns of the matrix evec into ascending or descending order according to the value of the parameter type as shown above. Only GSL::EIGEN_SORT_ABS_ASC and GSL::EIGEN_SORT_ABS_DESC are supported due to the eigenvalues being complex.

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