There is one prototype of sygvd
            available, please see below. 
sygvd( const int_t itype, const char jobz, MatrixA& a, MatrixB& b, VectorW& w );
            sygvd (short for $FRIENDLY_NAME)
            provides a C++ interface to LAPACK routines SSYGVD and DSYGVD. sygvd computes all the eigenvalues,
            and optionally, the eigenvectors of a real generalized symmetric-definite
            eigenproblem, of the form A*x(lambda)*B*x, A*Bx(lambda)*x,
            or B*A*x=(lambda)*x. Here A and B are assumed to be symmetric and B is
            also positive definite. If eigenvectors are desired, it uses a divide
            and conquer algorithm.
          
The divide and conquer algorithm makes very mild assumptions about floating point arithmetic. It will work on machines with a guard digit in add/subtract, or on those binary machines without guard digits which subtract like the Cray X-MP, Cray Y-MP, Cray C-90, or Cray-2. It could conceivably fail on hexadecimal or decimal machines without guard digits, but we know of none.
            The selection of the LAPACK routine is done during compile-time, and
            is determined by the type of values contained in type MatrixA.
            The type of values is obtained through the value_type
            meta-function typename value_type<MatrixA>::type. The dispatching table below illustrates
            to which specific routine the code path will be generated.
          
            Defined in header boost/numeric/bindings/lapack/driver/sygvd.hpp.
          
Parameters
The definition of term 1
The definition of term 2
The definition of term 3.
Definitions may contain paragraphs.
#include <boost/numeric/bindings/lapack/driver/sygvd.hpp> using namespace boost::numeric::bindings; lapack::sygvd( x, y, z );
this will output
[5] 0 1 2 3 4 5