SYEV   Eigenvalues and eigenvectors of symmetric/Hermitian matrix.

	[D] = SYEV(A) computes the eigenvalues of a symmetric/Hermitian matrix A.
	First A is reduced to tridiagonal form so that A=Q1*T*Q1' then the
	symmetric QR iteration algorithm is used on the tridiagonal matrix T to
	find D, the eigenvalues of T. D corresponds to the eigenvalues of A. This
	routine calls LAPACK routine _SYEV/_HEEV.

	[V,D] = SYEV(A) computes eigenvalues and eigenvectors of a
	symmetric/Hermitian matrix A. First A is reduced to tridiagonal form so
	that A = Q1*T*Q1' then the symmetric QR iteration algorithm is used on the
	tridiagonal matrix T to find D, the eigenvalues of T, and Q2 the
	eigenvectors of T so that T = Q2*D*Q2'. D corresponds to the eigenvalues of
	A. The eigenvectors of A are such that V = Q1*Q2.
	
	[V,D] = SYEV(A,PARAM) computes eigenvalues and eigenvectors of a
	symmetric/Hermitian matrix A. Same as [V,D] = SYEV(A) except that PARAM
	enables to choose the method used in the symmetric tridiagonal
	eigenproblem.
	PARAM controls the algorithm used:

       'dc'
             stands for "Divide and Conquer"
             calls LAPACK routine _SYEVD/_HEEVD

       'mrrr'
             stands for "Multiple Relatively Robust Representations"
             calls LAPACK routine _SYEVR/_HEEVR

       'bx'
             stands for "bisection and inverse iteration"
             calls LAPACK routine _SYEVX/_HEEVX

       'qr'
             (default)
             stands for " symmetric QR iteration"
             calls LAPACK routine _SYEV/_HEEV

    When A is symmetric/Hermitian SYEV(A,'qr') is the same as EIG(A).

    See also: EIG.

		

 GELS   Solves an overdetermined or underdetermined linear problem.

    [X] = GELS(A,B) same as [X] = GELS(A,B,'default') 

    [X] = GELS(A,B,PARAM) if A is m-by-n and m > n then GELS computes the solution of the
    linear least squares problem
            min || A * X - B ||_F ,
    if A is m-by-n and m < n then GELS computes the solution of the minimum norm problem
            min || X ||_F s.t. A*X = B
    PARAM controls the algorithm used:
	
       'dc'
             performs SVD factorization of A using the divide and conquer
             method then solve the undetermined or overdetermined problem.
             calls LAPACK routine _GELSD

       'svd'
             performs SVD factorization of A using QR iterations then solve the
             undetermined or overdetermined problem.
             calls LAPACK routine _GELSS

       'y'
             performs QR factorization of A then solve the undetermined or
             overdetermined problem.
             calls LAPACK routine _GELSY

       'default'
             (default)
             performs QR factorization of A without pivoting, then solve the
             undetermined or overdetermined problem.  The fact that there is no
             pivoting in the QR factorization means that A(:,1:min(size(A))
             needs to be full rank.
             calls LAPACK routine _GELS

    When A is m-by-n with m > n then [X] = GELS(A,B) is the same as X=A\B.

    When A is m-by-n with m < n  then [X] = GELS(A,B,PARAM) is _not_ the same as
    X=A\B. [X] = GELS(A,B,PARAM) solves the problem
    (*)    min_X || X ||_F s.t. AX=B
    while X=A\B solves AX=B by first performing a QR factorization of A
          [Q,R,E] = QR(A)
    then X is obtained via
          X = E(:,1:m)*(R(:,1:m)\(Q'*b))] 
	The \ method is faster than GELS, X is such that AX=B but X is not (in the
	general case) the solution of (*).

    See also: \, MLDIVIDE.