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-
- _S_p_e_c_t_r_a_l _D_e_c_o_m_p_o_s_i_t_i_o_n _o_f _a _M_a_t_r_i_x
-
- eigen(x, symmetric=all(x==t(x)), only.values=FALSE)
- eigen.rs(x, only.values=FALSE)
- eigen.rg(x, only.values=FALSE)
-
- _A_r_g_u_m_e_n_t_s:
-
- x : a matrix whose spectral decomposition is to
- be computed.
-
- symmetric : if TRUE, the matrix is assumed to be sym-
- metric and only its lower triangle is used.
-
- only.values : if TRUE, only the eigenvalues are computed
- and returned, otherwise both eigenvalues and
- eigenvectors are returned.
-
- _D_e_s_c_r_i_p_t_i_o_n:
-
- These functions provide an interface to the EISPACK
- routines RS and RG. The functions eigen.rs and
- eigen.rg provide versions of the decomposition for the
- symmetric and general cases. They provide a slight
- performance advantage by skipping the symmetry test of
- eigen.
-
- _V_a_l_u_e:
-
- The spectral decomposition of x is returned as com-
- ponents of a list. If the eigenvalues are all real the
- list contains components; values, a vector containing
- the eigenvalues of x and vectors, a matrix whose
- columns contain the eigenvectors of x.
-
- If some of the eigenvalues are complex, the list con-
- tains additional components; values.im and vectors.im,
- which respectively contain the imaginary parts of the
- eigenvalues and eigenvectors of x.
-
- _R_e_f_e_r_e_n_c_e_s:
-
- Smith, B. T, J. M. Boyle, J. J. Dongarra, B. S. Garbow,
- Y. Ikebe, V. Klema, C. B. Moler (1976). Matrix Eigen-
- systems Routines - EISPACK Guide. Springer-Verlag Lec-
- ture Notes in Computer Science.
-
- _S_e_e _A_l_s_o:
-
- svd, qr.
-
-