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